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	file = {[Coifman.2006] Diffusion maps.pdf:/home/kisung/Dropbox/VP. Packages/Rdimtools/[Coifman.2006] Diffusion maps.pdf:application/pdf}
}

@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},
	number = {6},
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	author = {Belkin, Mikhail and Niyogi, Partha},
	month = jun,
	year = {2003},
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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},
	author = {Hinton, Geoffrey E. and Roweis, Sam T.},
	editor = {Becker, S. and Thrun, S. and Obermayer, K.},
	year = {2003},
	pages = {857--864},
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}

@article{roweis_nonlinear_2000,
	title = {Nonlinear {Dimensionality} {Reduction} by {Locally} {Linear} {Embedding}},
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	month = dec,
	year = {2000},
	pages = {2323--2326},
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}

@article{vandermaaten_dimensionality_2009,
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	year = {2009},
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@article{vandermaaten_visualizing_2008,
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	year = {2008},
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@article{vandermaaten_learning_2009,
	title = {Learning a parametric embedding by preserving local structure},
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@inproceedings{bengio_outofsample_2003,
	title = {Out-of-{Sample} {Extensions} for {LLE}, {Isomap}, {MDS}, {Eigenmaps}, and {Spectral} {Clustering}},
	booktitle = {Advances in {Neural} {Information} {Processing} {Systems} 16 [{Neural} {Information} {Processing} {Systems}, {NIPS} 2003, {December} 8-13, 2003, {Vancouver} and {Whistler}, {British} {Columbia}, {Canada}]},
	author = {Bengio, Yoshua and Paiement, Jean-François and Vincent, Pascal and Delalleau, Olivier and Roux, Nicolas Le and Ouimet, Marie},
	year = {2003},
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@incollection{silva_global_2003,
	title = {Global {Versus} {Local} {Methods} in {Nonlinear} {Dimensionality} {Reduction}},
	booktitle = {Advances in {Neural} {Information} {Processing} {Systems} 15},
	publisher = {MIT Press},
	author = {Silva, Vin D. and Tenenbaum, Joshua B.},
	editor = {Becker, S. and Thrun, S. and Obermayer, K.},
	year = {2003},
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	year = {2006},
	keywords = {random projections, rates of convergence, sampling},
	pages = {287--296}
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@book{hyvarinen_independent_2001,
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	publisher = {J. Wiley},
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	year = {2001},
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}

@article{jenssen_kernel_2010,
	title = {Kernel {Entropy} {Component} {Analysis}},
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@inproceedings{walder_diffeomorphic_2009,
	address = {Red Hook, NY, USA},
	title = {Diffeomorphic {Dimensionality} {Reduction}},
	booktitle = {Advances in neural information processing systems 21},
	publisher = {Biologische Kybernetik},
	author = {Walder, C. and Schölkopf, B.},
	year = {2009},
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@inproceedings{cayton_robust_2006,
	address = {New York, NY, USA},
	series = {{ICML} '06},
	title = {Robust {Euclidean} {Embedding}},
	booktitle = {Proceedings of the 23rd {International} {Conference} on {Machine} {Learning}},
	publisher = {ACM},
	author = {Cayton, Lawrence and Dasgupta, Sanjoy},
	year = {2006},
	pages = {169--176},
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@article{daspremont_direct_2007,
	title = {A {Direct} {Formulation} for {Sparse} {PCA} {Using} {Semidefinite} {Programming}},
	volume = {49},
	number = {3},
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	author = {d'Aspremont, Alexandre and El Ghaoui, Laurent and Jordan, Michael I. and Lanckriet, Gert R. G.},
	month = jan,
	year = {2007},
	pages = {434--448},
	file = {[d'Aspremont.2007] A Direct Formulation for Sparse PCA Using Semidefinite Programming.pdf:/home/kisung/Dropbox/VP. Packages/Rdimtools/[d'Aspremont.2007] A Direct Formulation for Sparse PCA Using Semidefinite Programming.pdf:application/pdf}
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@article{zou_sparse_2006,
	title = {Sparse {Principal} {Component} {Analysis}},
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	number = {2},
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	month = jun,
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}

@inproceedings{cai_locality_2007,
	address = {San Francisco, CA, USA},
	series = {{IJCAI}'07},
	title = {Locality {Sensitive} {Discriminant} {Analysis}},
	booktitle = {Proceedings of the 20th {International} {Joint} {Conference} on {Artifical} {Intelligence}},
	publisher = {Morgan Kaufmann Publishers Inc.},
	author = {Cai, Deng and He, Xiaofei and Zhou, Kun and Han, Jiawei and Bao, Hujun},
	year = {2007},
	pages = {708--713},
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@article{xiaofeihe_learning_2008,
	title = {Learning a {Maximum} {Margin} {Subspace} for {Image} {Retrieval}},
	volume = {20},
	number = {2},
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	author = {{Xiaofei He} and {Deng Cai} and {Jiawei Han}},
	month = feb,
	year = {2008},
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@inproceedings{cai_isometric_2007,
	address = {Vancouver, British Columbia, Canada},
	series = {{AAAI}'07},
	title = {Isometric {Projection}},
	isbn = {978-1-57735-323-2},
	booktitle = {Proceedings of the {22Nd} {National} {Conference} on {Artificial} {Intelligence} - {Volume} 1},
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	author = {Cai, Deng and He, Xiaofei and Han, Jiawei},
	year = {2007},
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@article{cai_orthogonal_2006,
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	author = {Bishop, Christopher},
	month = jan,
	year = {1999},
	pages = {382--388},
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@article{tipping_probabilistic_1999,
	title = {Probabilistic {Principal} {Component} {Analysis}},
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	month = aug,
	year = {1999},
	pages = {611--622},
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@article{arenas-garcia_kernel_2013,
	title = {Kernel {Multivariate} {Analysis} {Framework} for {Supervised} {Subspace} {Learning}: {A} {Tutorial} on {Linear} and {Kernel} {Multivariate} {Methods}},
	volume = {30},
	number = {4},
	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},
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@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.2007] Minimum Volume Embedding.pdf:/home/kisung/Dropbox/VP. Packages/Rdimtools/[Shaw.2007] Minimum Volume Embedding.pdf:application/pdf}
}

@inproceedings{wang_feature_2007,
	title = {Feature {Extraction} by {Maximizing} the {Average} {Neighborhood} {Margin}},
	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.2007] Feature Extraction by Maximizing the Average Neighborhood Margin.pdf:/home/kisung/Dropbox/VP. Packages/Rdimtools/[Wang.2007] Feature Extraction by Maximizing the Average Neighborhood Margin.pdf:application/pdf}
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@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/VP. Packages/Rdimtools/[Goldberger.2004] Neighbourhood components analysis.pdf:application/pdf}
}

@article{wang_survey_2015,
	title = {Survey on distance metric learning and dimensionality reduction in data mining},
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	journal = {Data Mining and Knowledge Discovery},
	author = {Wang, Fei and Sun, Jimeng},
	month = mar,
	year = {2015},
	pages = {534--564},
	file = {[Wang.2015] Survey on distance metric learning and dimensionality reduction in data mining.pdf:/home/kisung/Dropbox/VP. Packages/Rdimtools/[Wang.2015] Survey on distance metric learning and dimensionality reduction in data mining.pdf:application/pdf}
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@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},
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@techreport{engel_survey_2012,
	title = {A {Survey} of {Dimension} {Reduction} {Methods} for {High}-dimensional {Data} {Analysis} and {Visualization}},
	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},
	file = {[Engel.2012] A Survey of Dimension Reduction Methods for High-dimensional Data Analysis and.pdf:/home/kisung/Dropbox/VP. Packages/Rdimtools/[Engel.2012] A Survey of Dimension Reduction Methods for High-dimensional Data Analysis and.pdf:application/pdf}
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@article{zheng_gabor_2007,
	title = {Gabor feature-based face recognition using supervised locality preserving projection},
	volume = {87},
	number = {10},
	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/VP. Packages/Rdimtools/[Zheng.2007] Gabor feature-based face recognition using supervised locality preserving.pdf:application/pdf}
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@inproceedings{shikkenawis_improving_2012,
	title = {Improving the {Locality} {Preserving} {Projection} for dimensionality reduction},
	booktitle = {2012 {Third} {International} {Conference} on {Emerging} {Applications} of {Information} {Technology}},
	publisher = {IEEE},
	author = {Shikkenawis, Gitam and Mitra, Suman K.},
	month = nov,
	year = {2012},
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@article{zhang_maximum_2007,
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	number = {6},
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	author = {Zhang, Tianhao},
	month = jun,
	year = {2007},
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@article{yang_globally_2007,
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	volume = {29},
	number = {4},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
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@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/VP. Packages/Rdimtools/[Lee.2002] Curvilinear Distance Analysis versus Isomap.pdf:application/pdf}
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@incollection{herault_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},
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	title = {Nonnegative {Embeddings} and {Projections} for {Dimensionality} {Reduction} and {Information} {Visualization}},
	booktitle = {2010 20th {International} {Conference} on {Pattern} {Recognition}},
	publisher = {IEEE},
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