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@article{weinberger_unsupervised_2006,
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@article{coifman_diffusion_2006,
	title = {Diffusion maps},
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	issn = {10635203},
	doi = {10.1016/j.acha.2006.04.006},
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	number = {1},
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	year = {2006},
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@inproceedings{belkin_convergence_2006,
	title = {Convergence of laplacian eigenmaps},
	booktitle = {In {NIPS}},
	author = {Belkin, Mikhail and Niyogi, Partha},
	year = {2006},
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@article{belkin_laplacian_2003,
	title = {Laplacian {Eigenmaps} for {Dimensionality} {Reduction} and {Data} {Representation}},
	volume = {15},
	issn = {0899-7667, 1530-888X},
	doi = {10.1162/089976603321780317},
	language = {en},
	number = {6},
	journal = {Neural Computation},
	author = {Belkin, Mikhail and Niyogi, Partha},
	month = jun,
	year = {2003},
	pages = {1373--1396},
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@incollection{hinton_stochastic_2003,
	title = {Stochastic {Neighbor} {Embedding}},
	url = {http://papers.nips.cc/paper/2276-stochastic-neighbor-embedding.pdf},
	booktitle = {Advances in {Neural} {Information} {Processing} {Systems} 15},
	publisher = {MIT Press},
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	editor = {Becker, S. and Thrun, S. and Obermayer, K.},
	year = {2003},
	pages = {857--864},
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@article{roweis_nonlinear_2000,
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@article{van_der_maaten_dimensionality_2009,
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	year = {2009},
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@article{van_der_maaten_visualizing_2008,
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@article{van_der_maaten_learning_2009,
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@inproceedings{bengio_out--sample_2003,
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	booktitle = {Advances in {Neural} {Information} {Processing} {Systems} 16 [{Neural} {Information} {Processing} {Systems}, {NIPS} 2003, {December} 8-13, 2003, {Vancouver} and {Whistler}, {British} {Columbia}, {Canada}]},
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	year = {2003},
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	title = {Global {Versus} {Local} {Methods} in {Nonlinear} {Dimensionality} {Reduction}},
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@book{hyvarinen_independent_2001,
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@inproceedings{walder_diffeomorphic_2009,
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	title = {Diffeomorphic {Dimensionality} {Reduction}},
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@inproceedings{cayton_robust_2006,
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	booktitle = {Proceedings of the 23rd {International} {Conference} on {Machine} {Learning}},
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@article{daspremont_direct_2007,
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	url = {http://epubs.siam.org/doi/10.1137/050645506},
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@article{zou_sparse_2006,
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@inproceedings{cai_locality_2007,
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	series = {{IJCAI}'07},
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	booktitle = {Proceedings of the 20th {International} {Joint} {Conference} on {Artifical} {Intelligence}},
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	author = {Cai, Deng and He, Xiaofei and Zhou, Kun and Han, Jiawei and Bao, Hujun},
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@article{xiaofei_he_learning_2008,
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@inproceedings{cai_isometric_2007,
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@article{cai_orthogonal_2006,
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	doi = {10.1007/11752790_2},
	pages = {34--51},
	file = {[Rosipal_Krämer.2006] Overview and Recent Advances in Partial Least Squares.pdf:/home/kisung/Dropbox/V3. Statistics/P3. Dimension Reduction/S1. Linear/13. PLS/[Rosipal_Krämer.2006] Overview and Recent Advances in Partial Least Squares.pdf:application/pdf}
}

@incollection{wold_path_1975,
	title = {Path {Models} with {Latent} {Variables}: {The} {NIPALS} {Approach}},
	isbn = {978-0-12-103950-9},
	shorttitle = {Path {Models} with {Latent} {Variables}},
	url = {http://linkinghub.elsevier.com/retrieve/pii/B9780121039509500174},
	language = {en},
	urldate = {2017-11-21},
	booktitle = {Quantitative {Sociology}},
	publisher = {Elsevier},
	author = {Wold, Herman},
	year = {1975},
	doi = {10.1016/B978-0-12-103950-9.50017-4},
	pages = {307--357}
}

@article{noonan_nipals_1977,
	title = {{NIPALS} {Path} {Modelling} with {Latent} {Variables}: {Analysing} {School} {Survey} {Data} {Using} {Nonlinear} {Iterative} {Partial} {Least} {Squares} $^{\textrm{1}}$},
	volume = {21},
	issn = {0031-3831, 1470-1170},
	shorttitle = {{NIPALS} {Path} {Modelling} with {Latent} {Variables}},
	url = {http://www.tandfonline.com/doi/full/10.1080/0031383770210103},
	doi = {10.1080/0031383770210103},
	language = {en},
	number = {1},
	urldate = {2017-11-21},
	journal = {Scandinavian Journal of Educational Research},
	author = {Noonan, Richard and Wold, Herman},
	month = jan,
	year = {1977},
	pages = {33--61}
}

@article{candes_robust_2011,
	title = {Robust principal component analysis?},
	volume = {58},
	issn = {00045411},
	url = {http://portal.acm.org/citation.cfm?doid=1970392.1970395},
	doi = {10.1145/1970392.1970395},
	language = {en},
	number = {3},
	urldate = {2017-12-03},
	journal = {Journal of the ACM},
	author = {Candès, Emmanuel J. and Li, Xiaodong and Ma, Yi and Wright, John},
	month = may,
	year = {2011},
	pages = {1--37},
	file = {[Candès.2011] Robust principal component analysis.pdf:/home/kisung/Dropbox/V3. Statistics/P3. Dimension Reduction/S2. Nonlinear/08. RPCA/[Candès.2011] Robust principal component analysis.pdf:application/pdf}
}

@inproceedings{bishop_bayesian_1999,
	title = {Bayesian {PCA}},
	volume = {11},
	url = {https://www.microsoft.com/en-us/research/publication/bayesian-pca/},
	abstract = {The technique of principal component analysis (PCA) has recently been expressed as the maximum likelihood solution for a generative latent variable model. In this paper we use this probabilistic reformulation as the basis for a Bayesian treatment of PCA. Our key result is that effective dimensionality of the latent space (equivalent to the number of retained principal components) can be determined automatically as part of the Bayesian inference procedure. An important application of this framework is to mixtures of probabilistic PCA models, in which each component can determine its own effective complexity.},
	booktitle = {Advances in {Neural} {Information} {Processing} {Systems}},
	publisher = {MIT Press},
	author = {Bishop, Christopher},
	month = jan,
	year = {1999},
	pages = {382--388},
	file = {[Bishop.1999] Bayesian PCA.pdf:/home/kisung/Dropbox/V3. Statistics/P3. Dimension Reduction/S1. Linear/21. PPCA+BPCA/[Bishop.1999] Bayesian PCA.pdf:application/pdf}
}

@article{tipping_probabilistic_1999,
	title = {Probabilistic {Principal} {Component} {Analysis}},
	volume = {61},
	issn = {1369-7412, 1467-9868},
	url = {http://doi.wiley.com/10.1111/1467-9868.00196},
	doi = {10.1111/1467-9868.00196},
	language = {en},
	number = {3},
	urldate = {2017-12-01},
	journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
	author = {Tipping, Michael E. and Bishop, Christopher M.},
	month = aug,
	year = {1999},
	pages = {611--622},
	file = {[Tipping_Bishop.1999] Probabilistic Principal Component Analysis.pdf:/home/kisung/Dropbox/V3. Statistics/P3. Dimension Reduction/S1. Linear/21. PPCA+BPCA/[Tipping_Bishop.1999] Probabilistic Principal Component Analysis.pdf:application/pdf}
}

@article{arenas-garcia_kernel_2013,
	title = {Kernel {Multivariate} {Analysis} {Framework} for {Supervised} {Subspace} {Learning}: {A} {Tutorial} on {Linear} and {Kernel} {Multivariate} {Methods}},
	volume = {30},
	issn = {1053-5888},
	shorttitle = {Kernel {Multivariate} {Analysis} {Framework} for {Supervised} {Subspace} {Learning}},
	url = {http://ieeexplore.ieee.org/document/6530763/},
	doi = {10.1109/MSP.2013.2250591},
	number = {4},
	urldate = {2017-12-01},
	journal = {IEEE Signal Processing Magazine},
	author = {Arenas-Garcia, Jeronimo and Petersen, Kaare Brandt and Camps-Valls, Gustavo and Hansen, Lars Kai},
	month = jul,
	year = {2013},
	pages = {16--29},
	file = {[Arenas-Garcia.2013] Kernel Multivariate Analysis Framework for Supervised Subspace Learning.pdf:/home/kisung/Dropbox/V3. Statistics/P3. Dimension Reduction/S2. Nonlinear/17. kMVA/[Arenas-Garcia.2013] Kernel Multivariate Analysis Framework for Supervised Subspace Learning.pdf:application/pdf}
}

@inproceedings{shaw_minimum_2007,
	title = {Minimum {Volume} {Embedding}},
	volume = {2 of JMLR: W\&CP},
	booktitle = {Proceedings of the {Eleventh} {International} {Conference} on {Artificial} {Intelligence} and {Statistics} {March} 21-24, 2007, {San} {Juan}, {Puerto} {Rico}},
	author = {Shaw, B. and Jebara, T.},
	editor = {Meila, Marina and Shen, Xiaotong},
	month = mar,
	year = {2007},
	pages = {460--467},
	file = {[Shaw_Jebara.2007] Minimum Volume Embedding.pdf:/home/kisung/Dropbox/V3. Statistics/P3. Dimension Reduction/S2. Nonlinear/50. MVE/[Shaw_Jebara.2007] Minimum Volume Embedding.pdf:application/pdf}
}

@inproceedings{wang_feature_2007,
	title = {Feature {Extraction} by {Maximizing} the {Average} {Neighborhood} {Margin}},
	doi = {10.1109/CVPR.2007.383124},
	booktitle = {2007 {IEEE} {Conference} on {Computer} {Vision} and {Pattern} {Recognition}},
	author = {Wang, F. and Zhang, C.},
	month = jun,
	year = {2007},
	keywords = {Data mining, ANMM, average neighborhood margin maximization, computer vision, Computer vision, Covariance matrix, face recognition, feature extraction, Feature extraction, Kernel, linear discriminant analysis, Linear discriminant analysis, optimisation, Pattern recognition, Principal component analysis, Scattering, supervised linear feature extraction, Tensile stress},
	pages = {1--8},
	file = {[Wang_Zhang.2007] Feature Extraction by Maximizing the Average Neighborhood Margin.pdf:/home/kisung/Dropbox/V3. Statistics/P3. Dimension Reduction/S1. Linear/20. ANMM/[Wang_Zhang.2007] Feature Extraction by Maximizing the Average Neighborhood Margin.pdf:application/pdf}
}

@inproceedings{goldberger_neighbourhood_2004,
	title = {Neighbourhood components analysis},
	booktitle = {Advances in {Neural} {Information} {Processing} {Systems} 17},
	publisher = {MIT Press},
	author = {Goldberger, Jacob and Roweis, Sam and Hinton, Geoff and Salakhutdinov, Ruslan},
	year = {2004},
	pages = {513--520},
	file = {[Goldberger.2004] Neighbourhood components analysis.pdf:/home/kisung/Dropbox/V3. Statistics/P3. Dimension Reduction/S1. Linear/19. NCA/[Goldberger.2004] Neighbourhood components analysis.pdf:application/pdf}
}

@article{wang_survey_2015,
	title = {Survey on distance metric learning and dimensionality reduction in data mining},
	volume = {29},
	issn = {1384-5810, 1573-756X},
	url = {http://link.springer.com/10.1007/s10618-014-0356-z},
	doi = {10.1007/s10618-014-0356-z},
	language = {en},
	number = {2},
	urldate = {2017-11-27},
	journal = {Data Mining and Knowledge Discovery},
	author = {Wang, Fei and Sun, Jimeng},
	month = mar,
	year = {2015},
	pages = {534--564},
	file = {[Wang_Sun.2015] Survey on distance metric learning and dimensionality reduction in data mining.pdf:/home/kisung/Dropbox/V3. Statistics/P3. Dimension Reduction/S3. Notes/7. metric learning/[Wang_Sun.2015] Survey on distance metric learning and dimensionality reduction in data mining.pdf:application/pdf}
}

@article{einbeck_representing_2007,
	title = {Representing complex data using localized principal components with application to astronomical data},
	journal = {ArXiv e-prints},
	author = {Einbeck, J. and Evers, L. and Bailer-Jones, C.},
	year = {2007},
	keywords = {Astrophysics},
	file = {[Einbeck.2007] Representing complex data using localized principal components with application.pdf:/home/kisung/Dropbox/V3. Statistics/P3. Dimension Reduction/S2. Nonlinear/49. LPCA/[Einbeck.2007] Representing complex data using localized principal components with application.pdf:application/pdf}
}

@techreport{engel_survey_2012,
	title = {A {Survey} of {Dimension} {Reduction} {Methods} for {High}-dimensional {Data} {Analysis} and {Visualization}},
	abstract = {Dimension reduction is commonly defined as the process of mapping high-dimensional data to a lower-dimensional embedding. Applications of dimension reduction include, but are not limited to, filtering, compression, regression, classification, feature analysis, and visualization. We review methods that compute a point-based visual representation of high-dimensional data sets to aid in exploratory data analysis. The aim is not to be exhaustive but to provide an overview of basic approaches, as well as to review select state-of-the-art methods. Our survey paper is an introduction to dimension reduction from a visualization point of view. Subsequently, a comparison of state-of-the-art methods outlines relations and shared research foci.},
	language = {eng},
	institution = {Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik GmbH, Wadern/Saarbruecken, Germany},
	author = {Engel, Daniel and Hüttenberger, Lars and Hamann, Bernd and Herbstritt, Marc},
	year = {2012},
	doi = {10.4230/OASIcs.VLUDS.2011.135},
	pages = {--},
	file = {[Engel.2012] A Survey of Dimension Reduction Methods for High-dimensional Data Analysis and.pdf:/home/kisung/Dropbox/V3. Statistics/P3. Dimension Reduction/[Engel.2012] A Survey of Dimension Reduction Methods for High-dimensional Data Analysis and.pdf:application/pdf}
}

@article{zheng_gabor_2007,
	title = {Gabor feature-based face recognition using supervised locality preserving projection},
	volume = {87},
	issn = {01651684},
	url = {http://linkinghub.elsevier.com/retrieve/pii/S0165168407000977},
	doi = {10.1016/j.sigpro.2007.03.006},
	language = {en},
	number = {10},
	urldate = {2017-12-18},
	journal = {Signal Processing},
	author = {Zheng, Zhonglong and Yang, Fan and Tan, Wenan and Jia, Jiong and Yang, Jie},
	month = oct,
	year = {2007},
	pages = {2473--2483},
	file = {[Zheng.2007] Gabor feature-based face recognition using supervised locality preserving.pdf:/home/kisung/Dropbox/V3. Statistics/P3. Dimension Reduction/S1. Linear/28. FAMILY_LPP/[Zheng.2007] Gabor feature-based face recognition using supervised locality preserving.pdf:application/pdf}
}

@inproceedings{shikkenawis_improving_2012,
	title = {Improving the {Locality} {Preserving} {Projection} for dimensionality reduction},
	isbn = {978-1-4673-1827-3 978-1-4673-1828-0 978-1-4673-1826-6},
	url = {http://ieeexplore.ieee.org/document/6407886/},
	doi = {10.1109/EAIT.2012.6407886},
	urldate = {2017-12-18},
	booktitle = {2012 {Third} {International} {Conference} on {Emerging} {Applications} of {Information} {Technology}},
	publisher = {IEEE},
	author = {Shikkenawis, Gitam and Mitra, Suman K.},
	month = nov,
	year = {2012},
	pages = {161--164},
	file = {[Shikkenawis_Mitra.2012] Improving the Locality Preserving Projection for dimensionality reduction.pdf:/home/kisung/Dropbox/V3. Statistics/P3. Dimension Reduction/S1. Linear/28. FAMILY_LPP/[Shikkenawis_Mitra.2012] Improving the Locality Preserving Projection for dimensionality reduction.pdf:application/pdf}
}

@article{zhang_maximum_2007,
	title = {Maximum variance projections for face recognition},
	volume = {46},
	issn = {0091-3286},
	url = {http://opticalengineering.spiedigitallibrary.org/article.aspx?doi=10.1117/1.2746880},
	doi = {10.1117/1.2746880},
	language = {en},
	number = {6},
	urldate = {2017-12-17},
	journal = {Optical Engineering},
	author = {Zhang, Tianhao},
	month = jun,
	year = {2007},
	pages = {067206},
	file = {[Zhang.2007] Maximum variance projections for face recognition.pdf:/home/kisung/Dropbox/V3. Statistics/P3. Dimension Reduction/S1. Linear/27. MVP/[Zhang.2007] Maximum variance projections for face recognition.pdf:application/pdf}
}

@article{yang_globally_2007,
	title = {Globally {Maximizing}, {Locally} {Minimizing}: {Unsupervised} {Discriminant} {Projection} with {Applications} to {Face} and {Palm} {Biometrics}},
	volume = {29},
	issn = {0162-8828, 2160-9292},
	shorttitle = {Globally {Maximizing}, {Locally} {Minimizing}},
	url = {http://ieeexplore.ieee.org/document/4107569/},
	doi = {10.1109/TPAMI.2007.1008},
	number = {4},
	urldate = {2017-12-16},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	author = {Yang, Jian and Zhang, David and Yang, Jing-yu and Niu, Ben},
	month = apr,
	year = {2007},
	pages = {650--664},
	file = {[Yang.2007] Globally Maximizing, Locally Minimizing.pdf:/home/kisung/Dropbox/V3. Statistics/P3. Dimension Reduction/S1. Linear/26. UDP/[Yang.2007] Globally Maximizing, Locally Minimizing.pdf:application/pdf}
}

@inproceedings{lee_curvilinear_2002,
	title = {Curvilinear {Distance} {Analysis} versus {Isomap}},
	booktitle = {{ESANN}},
	author = {Lee, John Aldo and Lendasse, Amaury and Verleysen, Michel},
	year = {2002},
	file = {[Lee.2002] Curvilinear Distance Analysis versus Isomap.pdf:/home/kisung/Dropbox/V3. Statistics/P3. Dimension Reduction/S2. Nonlinear/24. CRCA/[Lee.2002] Curvilinear Distance Analysis versus Isomap.pdf:application/pdf}
}

@incollection{goos_curvilinear_1999,
	address = {Berlin, Heidelberg},
	title = {Curvilinear component analysis for high-dimensional data representation: {I}. {Theoretical} aspects and practical use in the presence of noise},
	volume = {1607},
	isbn = {978-3-540-66068-2 978-3-540-48772-2},
	shorttitle = {Curvilinear component analysis for high-dimensional data representation},
	url = {http://link.springer.com/10.1007/BFb0100530},
	urldate = {2017-12-15},
	booktitle = {Engineering {Applications} of {Bio}-{Inspired} {Artificial} {Neural} {Networks}},
	publisher = {Springer Berlin Heidelberg},
	author = {Hérault, Jeanny and Jausions-Picaud, Claire and Guérin-Dugué, Anne},
	editor = {Goos, Gerhard and Hartmanis, Juris and van Leeuwen, Jan and Mira, José and Sánchez-Andrés, Juan V.},
	year = {1999},
	doi = {10.1007/BFb0100530},
	pages = {625--634}
}

@article{demartines_curvilinear_1997,
	title = {Curvilinear component analysis: a self-organizing neural network for nonlinear mapping of data sets},
	volume = {8},
	issn = {10459227},
	shorttitle = {Curvilinear component analysis},
	url = {http://ieeexplore.ieee.org/document/554199/},
	doi = {10.1109/72.554199},
	number = {1},
	urldate = {2017-12-15},
	journal = {IEEE Transactions on Neural Networks},
	author = {Demartines, P. and Herault, J.},
	month = jan,
	year = {1997},
	pages = {148--154},
	file = {[Demartines_Herault.1997] Curvilinear component analysis.pdf:/home/kisung/Dropbox/V3. Statistics/P3. Dimension Reduction/S2. Nonlinear/24. CRCA/[Demartines_Herault.1997] Curvilinear component analysis.pdf:application/pdf}
}

@article{lee_nonlinear_2004,
	title = {Nonlinear projection with curvilinear distances: {Isomap} versus curvilinear distance analysis},
	volume = {57},
	issn = {09252312},
	shorttitle = {Nonlinear projection with curvilinear distances},
	url = {http://linkinghub.elsevier.com/retrieve/pii/S0925231204000645},
	doi = {10.1016/j.neucom.2004.01.007},
	language = {en},
	urldate = {2017-12-15},
	journal = {Neurocomputing},
	author = {Lee, John Aldo and Lendasse, Amaury and Verleysen, Michel},
	month = mar,
	year = {2004},
	pages = {49--76},
	file = {[Lee.2004] Nonlinear projection with curvilinear distances.pdf:/home/kisung/Dropbox/V3. Statistics/P3. Dimension Reduction/S2. Nonlinear/24. CRCA/[Lee.2004] Nonlinear projection with curvilinear distances.pdf:application/pdf}
}

@book{borg_modern_2010,
	address = {New York, NY},
	title = {Modern multidimensional scaling: theory and applications},
	isbn = {978-1-4419-2046-1 978-0-387-28981-6},
	shorttitle = {Modern multidimensional scaling},
	abstract = {The book provides a comprehensive treatment of multidimensional scaling (MDS), a statistical technique used to analyze the structure of similarity or dissimilarity data in multidimensional space. Such data are widespread, for example, intercorrelations of attitude items, direct ratings of similarity on choice objects, or trade indices for a set of countries. MDS models such data as distances among points in a geometric space of low dimensionality. This makes complex data sets accessible to visual exploration and thus aids in seeing structure not obvious from the numbers. Other uses of MDS interpret the geometry and, in particular, the distance function as a psychological composition rule. The book may be used as an introduction to MDS for students in many areas including statistics, psychology, sociology, political sciences, and marketing. The prerequisite is a two-semester course in statistics for the social or managerial sciences. The book is also suited for several varieties of advanced courses on MDS, either with an emphasis on data analysis or with a focus on the psychology of similarity. All the mathematics required for more advanced topics is developed systematically.},
	language = {English},
	publisher = {Springer New York},
	author = {Borg, Ingwer and Groenen, Patrick J. F},
	year = {2010},
	note = {OCLC: 731879377},
	file = {[Borg.2010] Modern multidimensional scaling.pdf:/home/kisung/Dropbox/VBooks. Academic/[Borg.2010] Modern multidimensional scaling.pdf:application/pdf}
}

@book{borg_modern_2010-1,
	address = {New York, NY},
	title = {Modern multidimensional scaling: theory and applications},
	isbn = {978-1-4419-2046-1 978-0-387-28981-6},
	shorttitle = {Modern multidimensional scaling},
	abstract = {The book provides a comprehensive treatment of multidimensional scaling (MDS), a statistical technique used to analyze the structure of similarity or dissimilarity data in multidimensional space. Such data are widespread, for example, intercorrelations of attitude items, direct ratings of similarity on choice objects, or trade indices for a set of countries. MDS models such data as distances among points in a geometric space of low dimensionality. This makes complex data sets accessible to visual exploration and thus aids in seeing structure not obvious from the numbers. Other uses of MDS interpret the geometry and, in particular, the distance function as a psychological composition rule. The book may be used as an introduction to MDS for students in many areas including statistics, psychology, sociology, political sciences, and marketing. The prerequisite is a two-semester course in statistics for the social or managerial sciences. The book is also suited for several varieties of advanced courses on MDS, either with an emphasis on data analysis or with a focus on the psychology of similarity. All the mathematics required for more advanced topics is developed systematically.},
	language = {English},
	publisher = {Springer New York},
	author = {Borg, Ingwer and Groenen, Patrick J. F},
	year = {2010},
	note = {OCLC: 731879377}
}

@article{france_two-way_2011,
	title = {Two-{Way} {Multidimensional} {Scaling}: {A} {Review}},
	volume = {41},
	issn = {1094-6977, 1558-2442},
	shorttitle = {Two-{Way} {Multidimensional} {Scaling}},
	url = {http://ieeexplore.ieee.org/document/5613204/},
	doi = {10.1109/TSMCC.2010.2078502},
	number = {5},
	urldate = {2017-12-14},
	journal = {IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews)},
	author = {France, Stephen L. and Carroll, J. Douglas},
	month = sep,
	year = {2011},
	pages = {644--661},
	file = {[France_Carroll.2011] Two-Way Multidimensional Scaling.pdf:/home/kisung/Dropbox/V3. Statistics/P3. Dimension Reduction/S2. Nonlinear/25. MDS family/[France_Carroll.2011] Two-Way Multidimensional Scaling.pdf:application/pdf}
}

@article{kambhatla_dimension_1997,
	title = {Dimension {Reduction} by {Local} {Principal} {Component} {Analysis}},
	volume = {9},
	issn = {0899-7667, 1530-888X},
	url = {http://www.mitpressjournals.org/doi/10.1162/neco.1997.9.7.1493},
	doi = {10.1162/neco.1997.9.7.1493},
	language = {en},
	number = {7},
	urldate = {2017-12-14},
	journal = {Neural Computation},
	author = {Kambhatla, Nandakishore and Leen, Todd K.},
	month = oct,
	year = {1997},
	pages = {1493--1516},
	file = {[Kambhatla_Leen.1997] Dimension Reduction by Local Principal Component Analysis.pdf:/home/kisung/Dropbox/V3. Statistics/P3. Dimension Reduction/S2. Nonlinear/49. LPCA/[Kambhatla_Leen.1997] Dimension Reduction by Local Principal Component Analysis.pdf:application/pdf}
}

@article{agrafiotis_self-organizing_2002,
	title = {A self-organizing principle for learning nonlinear manifolds},
	volume = {99},
	issn = {0027-8424, 1091-6490},
	url = {http://www.pnas.org/cgi/doi/10.1073/pnas.242424399},
	doi = {10.1073/pnas.242424399},
	language = {en},
	number = {25},
	urldate = {2017-12-14},
	journal = {Proceedings of the National Academy of Sciences},
	author = {Agrafiotis, D. K. and Xu, H.},
	month = dec,
	year = {2002},
	pages = {15869--15872},
	file = {[Agrafiotis_Xu.2002] A self-organizing principle for learning nonlinear manifolds.pdf:/home/kisung/Dropbox/V3. Statistics/P3. Dimension Reduction/S2. Nonlinear/09. SPE/[Agrafiotis_Xu.2002] A self-organizing principle for learning nonlinear manifolds.pdf:application/pdf}
}

@article{agrafiotis_stochastic_2010,
	title = {Stochastic {Proximity} {Embedding}: {Methods} and {Applications}},
	volume = {29},
	issn = {18681743},
	shorttitle = {Stochastic {Proximity} {Embedding}},
	url = {http://doi.wiley.com/10.1002/minf.201000134},
	doi = {10.1002/minf.201000134},
	language = {en},
	number = {11},
	urldate = {2017-12-14},
	journal = {Molecular Informatics},
	author = {Agrafiotis, Dimitris K. and Xu, Huafeng and Zhu, Fangqiang and Bandyopadhyay, Deepak and Liu, Pu},
	month = nov,
	year = {2010},
	pages = {758--770},
	file = {[Agrafiotis.2010] Stochastic Proximity Embedding.pdf:/home/kisung/Dropbox/V3. Statistics/P3. Dimension Reduction/S2. Nonlinear/09. SPE/[Agrafiotis.2010] Stochastic Proximity Embedding.pdf:application/pdf}
}

@article{agrafiotis_stochastic_2003,
	title = {Stochastic proximity embedding},
	volume = {24},
	issn = {0192-8651, 1096-987X},
	url = {http://doi.wiley.com/10.1002/jcc.10234},
	doi = {10.1002/jcc.10234},
	language = {en},
	number = {10},
	urldate = {2017-12-14},
	journal = {Journal of Computational Chemistry},
	author = {Agrafiotis, Dimitris K.},
	month = jul,
	year = {2003},
	pages = {1215--1221},
	file = {[Agrafiotis.2003] Stochastic proximity embedding.pdf:/home/kisung/Dropbox/V3. Statistics/P3. Dimension Reduction/S2. Nonlinear/09. SPE/[Agrafiotis.2003] Stochastic proximity embedding.pdf:application/pdf}
}

@inproceedings{boutet_being_2016,
	title = {Being prepared in a sparse world: {The} case of {KNN} graph construction},
	isbn = {978-1-5090-2020-1},
	shorttitle = {Being prepared in a sparse world},
	url = {http://ieeexplore.ieee.org/document/7498244/},
	doi = {10.1109/ICDE.2016.7498244},
	urldate = {2017-12-19},
	booktitle = {{ICDE} 2016},
	publisher = {IEEE},
	author = {Boutet, Antoine and Kermarrec, Anne-Marie and Mittal, Nupur and Taiani, Francois},
	month = may,
	year = {2016},
	pages = {241--252},
	file = {[Boutet.2016] Being prepared in a sparse world.pdf:/home/kisung/Dropbox/V3. Statistics/P3. Dimension Reduction/S3. Notes/1. graph/[Boutet.2016] Being prepared in a sparse world.pdf:application/pdf}
}

@article{yu_enhanced_2011,
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	author = {Ding, C. and {Xiaofeng He} and {Hongyuan Zha} and Simon, H.D.},
	year = {2002},
	pages = {147--154},
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}

@inproceedings{li_document_2004,
	title = {Document clustering via adaptive subspace iteration},
	isbn = {978-1-58113-881-8},
	url = {http://portal.acm.org/citation.cfm?doid=1008992.1009031},
	doi = {10.1145/1008992.1009031},
	language = {en},
	urldate = {2018-01-13},
	booktitle = {Proceedings of the 27th annual international {ACM} {SIGIR} conference on {Research} and development in information retrieval},
	publisher = {ACM Press},
	author = {Li, Tao and Ma, Sheng and Ogihara, Mitsunori},
	year = {2004},
	pages = {218},
	file = {[Li.2004] Document clustering via adaptive subspace iteration.pdf:/home/kisung/Dropbox/V3. Statistics/P3. Dimension Reduction/S1. Linear/39. LDAKM/[Li.2004] Document clustering via adaptive subspace iteration.pdf:application/pdf}
}

@article{ma_review_2013,
	title = {A {Review} on {Dimension} {Reduction}: \textit{{A} {Review} on {Dimension} {Reduction}}},
	volume = {81},
	issn = {03067734},
	shorttitle = {A {Review} on {Dimension} {Reduction}},
	url = {http://doi.wiley.com/10.1111/j.1751-5823.2012.00182.x},
	doi = {10.1111/j.1751-5823.2012.00182.x},
	language = {en},
	number = {1},
	urldate = {2018-01-13},
	journal = {International Statistical Review},
	author = {Ma, Yanyuan and Zhu, Liping},
	month = apr,
	year = {2013},
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@article{min_locality_2004,
	title = {Locality pursuit embedding},
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	url = {http://linkinghub.elsevier.com/retrieve/pii/S0031320303003315},
	doi = {10.1016/j.patcog.2003.09.005},
	language = {en},
	number = {4},
	urldate = {2018-01-14},
	journal = {Pattern Recognition},
	author = {Min, Wanli and Lu, Ke and He, Xiaofei},
	month = apr,
	year = {2004},
	pages = {781--788},
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@article{zheng_supervised_2006,
	title = {Supervised locality pursuit embedding for pattern classification},
	volume = {24},
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	url = {http://linkinghub.elsevier.com/retrieve/pii/S0262885606000898},
	doi = {10.1016/j.imavis.2006.02.007},
	language = {en},
	number = {8},
	urldate = {2018-01-14},
	journal = {Image and Vision Computing},
	author = {Zheng, Zhonglong and Yang, Jie},
	month = aug,
	year = {2006},
	pages = {819--826},
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@article{lai_maximal_2011,
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	issn = {0932-8092, 1432-1769},
	url = {http://link.springer.com/10.1007/s00138-011-0332-1},
	doi = {10.1007/s00138-011-0332-1},
	language = {en},
	number = {4},
	urldate = {2018-01-18},
	journal = {Machine Vision and Applications},
	author = {Lai, Zhihui and Zhao, Cairong and Chen, Yi and Jin, Zhong},
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@inproceedings{cai_learning_2007,
	title = {Learning a {Spatially} {Smooth} {Subspace} for {Face} {Recognition}},
	isbn = {978-1-4244-1179-5 978-1-4244-1180-1},
	url = {http://ieeexplore.ieee.org/document/4270079/},
	doi = {10.1109/CVPR.2007.383054},
	urldate = {2018-01-18},
	booktitle = {2007 {IEEE} {Conference} on {Computer} {Vision} and {Pattern} {Recognition}},
	publisher = {IEEE},
	author = {Cai, Deng and He, Xiaofei and Hu, Yuxiao and Han, Jiawei and Huang, Thomas},
	month = jun,
	year = {2007},
	pages = {1--7},
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@article{raducanu_supervised_2012,
	title = {A supervised non-linear dimensionality reduction approach for manifold learning},
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	url = {http://linkinghub.elsevier.com/retrieve/pii/S0031320311005073},
	doi = {10.1016/j.patcog.2011.12.006},
	language = {en},
	number = {6},
	urldate = {2018-01-19},
	journal = {Pattern Recognition},
	author = {Raducanu, B. and Dornaika, F.},
	month = jun,
	year = {2012},
	pages = {2432--2444},
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@article{dornaika_enhanced_2013,
	title = {Enhanced and parameterless {Locality} {Preserving} {Projections} for face recognition},
	volume = {99},
	issn = {09252312},
	url = {http://linkinghub.elsevier.com/retrieve/pii/S0925231212005826},
	doi = {10.1016/j.neucom.2012.07.016},
	language = {en},
	urldate = {2018-01-20},
	journal = {Neurocomputing},
	author = {Dornaika, Fadi and Assoum, Ammar},
	month = jan,
	year = {2013},
	pages = {448--457},
	file = {[Dornaika_Assoum.2013] Enhanced and parameterless Locality Preserving Projections for face recognition.pdf:/home/kisung/Dropbox/V3. Statistics/P3. Dimension Reduction/S1. Linear/28. FAMILY_LPP/[Dornaika_Assoum.2013] Enhanced and parameterless Locality Preserving Projections for face recognition.pdf:application/pdf}
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@article{yang_collaborative_2015,
	title = {A collaborative representation based projections method for feature extraction},
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	url = {http://linkinghub.elsevier.com/retrieve/pii/S0031320314002684},
	doi = {10.1016/j.patcog.2014.07.009},
	language = {en},
	number = {1},
	urldate = {2018-01-20},
	journal = {Pattern Recognition},
	author = {Yang, Wankou and Wang, Zhenyu and Sun, Changyin},
	month = jan,
	year = {2015},
	pages = {20--27},
	file = {[Yang.2015] A collaborative representation based projections method for feature extraction.pdf:/home/kisung/Dropbox/V3. Statistics/P3. Dimension Reduction/S1. Linear/43. CRP/[Yang.2015] A collaborative representation based projections method for feature extraction.pdf:application/pdf}
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@article{haixian_wang_locality-preserved_2008,
	title = {Locality-{Preserved} {Maximum} {Information} {Projection}},
	volume = {19},
	issn = {1045-9227, 1941-0093},
	url = {http://ieeexplore.ieee.org/document/4439298/},
	doi = {10.1109/TNN.2007.910733},
	number = {4},
	urldate = {2018-01-21},
	journal = {IEEE Transactions on Neural Networks},
	author = {{Haixian Wang} and {Sibao Chen} and {Zilan Hu} and {Wenming Zheng}},
	month = apr,
	year = {2008},
	pages = {571--585},
	file = {[Haixian Wang.2008] Locality-Preserved Maximum Information Projection.pdf:/home/kisung/Dropbox/V3. Statistics/P3. Dimension Reduction/S1. Linear/44. LPMIP/[Haixian Wang.2008] Locality-Preserved Maximum Information Projection.pdf:application/pdf}
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@article{yang_locally_2006,
	title = {Locally principal component learning for face representation and recognition},
	volume = {69},
	issn = {09252312},
	url = {http://linkinghub.elsevier.com/retrieve/pii/S0925231206000919},
	doi = {10.1016/j.neucom.2006.01.009},
	language = {en},
	number = {13-15},
	urldate = {2018-01-21},
	journal = {Neurocomputing},
	author = {Yang, Jian and Zhang, David and Yang, Jing-yu},
	month = aug,
	year = {2006},
	pages = {1697--1701},
	file = {[Yang.2006] Locally principal component learning for face representation and recognition.pdf:/home/kisung/Dropbox/V3. Statistics/P3. Dimension Reduction/S1. Linear/45. LPCA/[Yang.2006] Locally principal component learning for face representation and recognition.pdf:application/pdf}
}

@article{zhang_semi-supervised_2011,
	title = {Semi-supervised locally discriminant projection for classification and recognition},
	volume = {24},
	issn = {09507051},
	url = {http://linkinghub.elsevier.com/retrieve/pii/S0950705110001681},
	doi = {10.1016/j.knosys.2010.11.002},
	language = {en},
	number = {2},
	urldate = {2018-01-21},
	journal = {Knowledge-Based Systems},
	author = {Zhang, Shanwen and Lei, Ying-Ke and Wu, Yan-Hua},
	month = mar,
	year = {2011},
	pages = {341--346},
	file = {[Zhang.2011] Semi-supervised locally discriminant projection for classification and.pdf:/home/kisung/Dropbox/V3. Statistics/P3. Dimension Reduction/S1. Linear/46. LDP/[Zhang.2011] Semi-supervised locally discriminant projection for classification and.pdf:application/pdf}
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@article{zhao_local_2006,
	title = {Local structure based supervised feature extraction},
	volume = {39},
	issn = {00313203},
	url = {http://linkinghub.elsevier.com/retrieve/pii/S0031320306000811},
	doi = {10.1016/j.patcog.2006.02.023},
	language = {en},
	number = {8},
	urldate = {2018-01-21},
	journal = {Pattern Recognition},
	author = {Zhao, Haitao and Sun, Shaoyuan and Jing, Zhongliang and Yang, Jingyu},
	month = aug,
	year = {2006},
	pages = {1546--1550},
	file = {[Zhao.2006] Local structure based supervised feature extraction.pdf:/home/kisung/Dropbox/V3. Statistics/P3. Dimension Reduction/S1. Linear/46. LDP/[Zhao.2006] Local structure based supervised feature extraction.pdf:application/pdf}
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@article{zhao_locality_2008,
	title = {Locality sensitive semi-supervised feature selection},
	volume = {71},
	issn = {09252312},
	url = {http://linkinghub.elsevier.com/retrieve/pii/S092523120800115X},
	doi = {10.1016/j.neucom.2007.06.014},
	language = {en},
	number = {10-12},
	urldate = {2018-01-21},
	journal = {Neurocomputing},
	author = {Zhao, Jidong and Lu, Ke and He, Xiaofei},
	month = jun,
	year = {2008},
	pages = {1842--1849},
	file = {[Zhao.2008] Locality sensitive semi-supervised feature selection.pdf:/home/kisung/Dropbox/V3. Statistics/P3. Dimension Reduction/S1. Linear/17. LSDA_LSDF/[Zhao.2008] Locality sensitive semi-supervised feature selection.pdf:application/pdf}
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@inproceedings{he_laplacian_2005,
	address = {Cambridge, MA, USA},
	series = {{NIPS}'05},
	title = {Laplacian {Score} for {Feature} {Selection}},
	url = {http://dl.acm.org/citation.cfm?id=2976248.2976312},
	booktitle = {Proceedings of the 18th {International} {Conference} on {Neural} {Information} {Processing} {Systems}},
	publisher = {MIT Press},
	author = {He, Xiaofei and Cai, Deng and Niyogi, Partha},
	year = {2005},
	pages = {507--514},
	file = {[He.2005] Laplacian Score for Feature Selection.pdf:/home/kisung/Dropbox/V3. Statistics/P3. Dimension Reduction/S1. Linear/47. FSCORE/[He.2005] Laplacian Score for Feature Selection.pdf:application/pdf}
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@article{valizade_hasanloei_combined_2017,
	title = {A combined {Fisher} and {Laplacian} score for feature selection in {QSAR} based drug design using compounds with known and unknown activities},
	issn = {1573-4951},
	doi = {10.1007/s10822-017-0094-6},
	journal = {Journal of Computer-Aided Molecular Design},
	author = {Valizade Hasanloei, Mohammad Amin and Sheikhpour, Razieh and Sarram, Mehdi Agha and Sheikhpour, Elnaz and Sharifi, Hamdollah},
	month = dec,
	year = {2017},
	file = {[Valizade Hasanloei.2017] A combined Fisher and Laplacian score for feature selection in QSAR based drug.pdf:/home/kisung/Dropbox/V3. Statistics/P3. Dimension Reduction/S1. Linear/47. FSCORE/[Valizade Hasanloei.2017] A combined Fisher and Laplacian score for feature selection in QSAR based drug.pdf:application/pdf}
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@inproceedings{cai_unsupervised_2010,
	title = {Unsupervised feature selection for multi-cluster data},
	isbn = {978-1-4503-0055-1},
	url = {http://dl.acm.org/citation.cfm?doid=1835804.1835848},
	doi = {10.1145/1835804.1835848},
	language = {en},
	urldate = {2018-01-21},
	booktitle = {Proceedings of the 16th {ACM} {SIGKDD} international conference on {Knowledge} discovery and data mining},
	publisher = {ACM Press},
	author = {Cai, Deng and Zhang, Chiyuan and He, Xiaofei},
	year = {2010},
	pages = {333--342},
	file = {[Cai.2010] Unsupervised feature selection for multi-cluster data.pdf:/home/kisung/Dropbox/V3. Statistics/P3. Dimension Reduction/S1. Linear/47. FSCORE/[Cai.2010] Unsupervised feature selection for multi-cluster data.pdf:application/pdf}
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@article{jin_face_2001,
	title = {Face recognition based on the uncorrelated discriminant transformation},
	volume = {34},
	issn = {0031-3203},
	url = {http://www.sciencedirect.com/science/article/pii/S0031320300000844},
	number = {7},
	journal = {Pattern Recognition},
	author = {Jin, Zhong and Yang, Jing-Yu and Hu, Zhong-Shan and Lou, Zhen},
	year = {2001},
	keywords = {Feature extraction, Pattern recognition, Dimensionality reduction, Discriminant analysis, Face recognition},
	pages = {1405 -- 1416},
	file = {[Jin.2001] Face recognition based on the uncorrelated discriminant transformation.pdf:/home/kisung/Dropbox/V3. Statistics/P3. Dimension Reduction/S1. Linear/34. FAMILY_LDA/[Jin.2001] Face recognition based on the uncorrelated discriminant transformation.pdf:application/pdf}
}

@article{ye_characterization_2005,
	title = {Characterization of a {Family} of {Algorithms} for {Generalized} {Discriminant} {Analysis} on {Undersampled} {Problems}},
	volume = {6},
	issn = {1532-4435},
	url = {http://dl.acm.org/citation.cfm?id=1046920.1088689},
	journal = {J. Mach. Learn. Res.},
	author = {Ye, Jieping},
	month = dec,
	year = {2005},
	pages = {483--502},
	file = {[Ye.2005] Characterization of a Family of Algorithms for Generalized Discriminant.pdf:/home/kisung/Dropbox/V3. Statistics/P3. Dimension Reduction/S1. Linear/34. FAMILY_LDA/[Ye.2005] Characterization of a Family of Algorithms for Generalized Discriminant.pdf:application/pdf}
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@article{kokiopoulou_orthogonal_2007,
	title = {Orthogonal {Neighborhood} {Preserving} {Projections}: {A} {Projection}-{Based} {Dimensionality} {Reduction} {Technique}},
	volume = {29},
	issn = {0162-8828},
	shorttitle = {Orthogonal {Neighborhood} {Preserving} {Projections}},
	url = {http://ieeexplore.ieee.org/document/4359299/},
	doi = {10.1109/TPAMI.2007.1131},
	number = {12},
	urldate = {2018-01-22},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	author = {Kokiopoulou, E. and Saad, Y.},
	month = dec,
	year = {2007},
	pages = {2143--2156},
	file = {[Kokiopoulou_Saad.2007] Orthogonal Neighborhood Preserving Projections.pdf:/home/kisung/Dropbox/V3. Statistics/P3. Dimension Reduction/S1. Linear/48. ONPP/[Kokiopoulou_Saad.2007] Orthogonal Neighborhood Preserving Projections.pdf:application/pdf}
}

@article{tianhao_zhang_discriminative_2010,
	title = {Discriminative {Orthogonal} {Neighborhood}-{Preserving} {Projections} for {Classification}},
	volume = {40},
	issn = {1083-4419},
	url = {http://ieeexplore.ieee.org/document/5233908/},
	doi = {10.1109/TSMCB.2009.2027473},
	number = {1},
	urldate = {2018-01-22},
	journal = {IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)},
	author = {{Tianhao Zhang} and {Kaiqi Huang} and {Xuelong Li} and {Jie Yang} and {Dacheng Tao}},
	month = feb,
	year = {2010},
	pages = {253--263},
	file = {[Tianhao Zhang.2010] Discriminative Orthogonal Neighborhood-Preserving Projections for Classification.pdf:/home/kisung/Dropbox/V3. Statistics/P3. Dimension Reduction/S1. Linear/48. ONPP/[Tianhao Zhang.2010] Discriminative Orthogonal Neighborhood-Preserving Projections for Classification.pdf:application/pdf}
}

@inproceedings{zafeiriou_nonnegative_2010,
	title = {Nonnegative {Embeddings} and {Projections} for {Dimensionality} {Reduction} and {Information} {Visualization}},
	isbn = {978-1-4244-7542-1},
	url = {http://ieeexplore.ieee.org/document/5596031/},
	doi = {10.1109/ICPR.2010.183},
	urldate = {2018-01-22},
	booktitle = {2010 20th {International} {Conference} on {Pattern} {Recognition}},
	publisher = {IEEE},
	author = {Zafeiriou, Stefanos and Laskaris, Nikolaos},
	month = aug,
	year = {2010},
	pages = {726--729},
	file = {[Zafeiriou_Laskaris.2010] Nonnegative Embeddings and Projections for Dimensionality Reduction and.pdf:/home/kisung/Dropbox/V3. Statistics/P3. Dimension Reduction/S1. Linear/49. NonnegExt/[Zafeiriou_Laskaris.2010] Nonnegative Embeddings and Projections for Dimensionality Reduction and.pdf:application/pdf}
}

@article{song_face_2007,
	title = {Face recognition based on a novel linear discriminant criterion},
	volume = {10},
	issn = {1433-7541, 1433-755X},
	url = {http://link.springer.com/10.1007/s10044-006-0057-3},
	doi = {10.1007/s10044-006-0057-3},
	language = {en},
	number = {3},
	urldate = {2018-01-23},
	journal = {Pattern Analysis and Applications},
	author = {Song, Fengxi and Zhang, David and Chen, Qinglong and Wang, Jizhong},
	month = jul,
	year = {2007},
	pages = {165--174},
	file = {[Song.2007] Face recognition based on a novel linear discriminant criterion.pdf:/home/kisung/Dropbox/V3. Statistics/P3. Dimension Reduction/S1. Linear/50. MSD/[Song.2007] Face recognition based on a novel linear discriminant criterion.pdf:application/pdf}
}

@article{fengxi_song_multiple_2007,
	title = {A {Multiple} {Maximum} {Scatter} {Difference} {Discriminant} {Criterion} for {Facial} {Feature} {Extraction}},
	volume = {37},
	issn = {1083-4419},
	url = {http://ieeexplore.ieee.org/document/4362695/},
	doi = {10.1109/TSMCB.2007.906579},
	number = {6},
	urldate = {2018-01-23},
	journal = {IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)},
	author = {{Fengxi Song} and Zhang, D. and {Dayong Mei} and {Zhongwei Guo}},
	month = dec,
	year = {2007},
	pages = {1599--1606},
	file = {[Fengxi Song.2007] A Multiple Maximum Scatter Difference Discriminant Criterion for Facial Feature.pdf:/home/kisung/Dropbox/V3. Statistics/P3. Dimension Reduction/S1. Linear/50. MSD/[Fengxi Song.2007] A Multiple Maximum Scatter Difference Discriminant Criterion for Facial Feature.pdf:application/pdf}
}

@inproceedings{zhao_locality_2009,
	address = {Berlin, Heidelberg},
	title = {Locality {Preserving} {Fisher} {Discriminant} {Analysis} for {Face} {Recognition}},
	isbn = {978-3-642-04070-2},
	abstract = {Dimensionality reduction is a key technology for face recognition. In this paper, we propose a novel method, called Locality Preserving Fisher Discriminant Analysis (LPFDA), which extends the original Fisher Discriminant Analysis by preserving the locality structure of the data. LPFDA can get a subspace projection matrix by solving a generalized eigenvalue problem. Several experiments are conducted to demonstrate the effectiveness and robustness of our method.},
	booktitle = {Emerging {Intelligent} {Computing} {Technology} and {Applications}},
	publisher = {Springer Berlin Heidelberg},
	author = {Zhao, Xu and Tian, Xiaoyan},
	editor = {Huang, De-Shuang and Jo, Kang-Hyun and Lee, Hong-Hee and Kang, Hee-Jun and Bevilacqua, Vitoantonio},
	year = {2009},
	pages = {261--269},
	file = {[Zhao_Tian.2009] Locality Preserving Fisher Discriminant Analysis for Face Recognition.pdf:/home/kisung/Dropbox/V3. Statistics/P3. Dimension Reduction/S1. Linear/28. FAMILY_LPP/[Zhao_Tian.2009] Locality Preserving Fisher Discriminant Analysis for Face Recognition.pdf:application/pdf}
}

@article{wiskott_slow_2002,
	title = {Slow {Feature} {Analysis}: {Unsupervised} {Learning} of {Invariances}},
	volume = {14},
	issn = {0899-7667, 1530-888X},
	shorttitle = {Slow {Feature} {Analysis}},
	url = {http://www.mitpressjournals.org/doi/10.1162/089976602317318938},
	doi = {10.1162/089976602317318938},
	language = {en},
	number = {4},
	urldate = {2018-01-23},
	journal = {Neural Computation},
	author = {Wiskott, Laurenz and Sejnowski, Terrence J.},
	month = apr,
	year = {2002},
	pages = {715--770},
	file = {[Wiskott_Sejnowski.2002] Slow Feature Analysis.pdf:/home/kisung/Dropbox/V3. Statistics/P3. Dimension Reduction/S1. Linear/11. FAMILY_SFA/[Wiskott_Sejnowski.2002] Slow Feature Analysis.pdf:application/pdf}
}

@article{sprekeler_understanding_2008,
	title = {Understanding {Slow} {Feature} {Analysis}: {A} {Mathematical} {Framework}},
	issn = {1556-5068},
	shorttitle = {Understanding {Slow} {Feature} {Analysis}},
	url = {https://www.ssrn.com/abstract=3076122},
	doi = {10.2139/ssrn.3076122},
	language = {en},
	urldate = {2018-01-23},
	journal = {SSRN Electronic Journal},
	author = {Sprekeler, Henning and Wiskott, Laurenz},
	year = {2008},
	file = {[Sprekeler_Wiskott.2008] Understanding Slow Feature Analysis.pdf:/home/kisung/Dropbox/V3. Statistics/P3. Dimension Reduction/S1. Linear/11. FAMILY_SFA/[Sprekeler_Wiskott.2008] Understanding Slow Feature Analysis.pdf:application/pdf}
}

@article{kompella_incremental_2012,
	title = {Incremental {Slow} {Feature} {Analysis}: {Adaptive} {Low}-{Complexity} {Slow} {Feature} {Updating} from {High}-{Dimensional} {Input} {Streams}},
	volume = {24},
	issn = {0899-7667, 1530-888X},
	shorttitle = {Incremental {Slow} {Feature} {Analysis}},
	url = {http://www.mitpressjournals.org/doi/10.1162/NECO_a_00344},
	doi = {10.1162/NECO_a_00344},
	language = {en},
	number = {11},
	urldate = {2018-01-23},
	journal = {Neural Computation},
	author = {Kompella, Varun Raj and Luciw, Matthew and Schmidhuber, Jürgen},
	month = nov,
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