
@article{mullner_fastcluster_2013,
	title = {fastcluster : {Fast} {Hierarchical}, {Agglomerative} {Clustering} {Routines} for {R} and {Python}},
	volume = {53},
	issn = {1548-7660},
	doi = {10.18637/jss.v053.i09},
	language = {en},
	number = {9},
	journal = {Journal of Statistical Software},
	author = {Müllner, Daniel},
	year = {2013},
	file = {[Müllner.2013] fastcluster.pdf:/home/kisung/Dropbox/VP. Packages/RiemBaseExt/[Müllner.2013] fastcluster.pdf:application/pdf}
}

@article{fletcher_geometric_2009,
	title = {The geometric median on {Riemannian} manifolds with application to robust atlas estimation},
	volume = {45},
	issn = {10538119},
	doi = {10.1016/j.neuroimage.2008.10.052},
	language = {en},
	number = {1},
	journal = {NeuroImage},
	author = {Fletcher, P. Thomas and Venkatasubramanian, Suresh and Joshi, Sarang},
	month = mar,
	year = {2009},
	pages = {S143--S152},
	file = {[Fletcher.2009] The geometric median on Riemannian manifolds with application to robust atlas.pdf:/home/kisung/Dropbox/VP. Packages/RiemBaseExt/[Fletcher.2009] The geometric median on Riemannian manifolds with application to robust atlas.pdf:application/pdf}
}

@article{aftab_generalized_2015,
	title = {Generalized {Weiszfeld} {Algorithms} for {Lq} {Optimization}},
	volume = {37},
	issn = {0162-8828, 2160-9292, 1939-3539},
	doi = {10.1109/TPAMI.2014.2353625},
	number = {4},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	author = {Aftab, Khurrum and Hartley, Richard and Trumpf, Jochen},
	month = apr,
	year = {2015},
	pages = {728--745},
	file = {[Aftab.2015] Generalized Weiszfeld Algorithms for Lq Optimization.pdf:/home/kisung/Dropbox/VP. Packages/RiemBaseExt/[Aftab.2015] Generalized Weiszfeld Algorithms for Lq Optimization.pdf:application/pdf}
}

@article{afsari_convergence_2013,
	title = {On the {Convergence} of {Gradient} {Descent} for {Finding} the {Riemannian} {Center} of {Mass}},
	volume = {51},
	issn = {0363-0129, 1095-7138},
	doi = {10.1137/12086282X},
	language = {en},
	number = {3},
	journal = {SIAM Journal on Control and Optimization},
	author = {Afsari, Bijan and Tron, Roberto and Vidal, René},
	month = jan,
	year = {2013},
	pages = {2230--2260},
	file = {[Afsari.2013] On the Convergence of Gradient Descent for Finding the Riemannian Center of Mass.pdf:/home/kisung/Dropbox/VP. Packages/RiemBaseExt/[Afsari.2013] On the Convergence of Gradient Descent for Finding the Riemannian Center of Mass.pdf:application/pdf}
}

@article{kendall_probability_1990,
	title = {Probability, {Convexity}, and {Harmonic} {Maps} with {Small} {Image} {I}: {Uniqueness} and {Fine} {Existence}},
	volume = {s3-61},
	issn = {00246115},
	shorttitle = {Probability, {Convexity}, and {Harmonic} {Maps} with {Small} {Image} {I}},
	doi = {10.1112/plms/s3-61.2.371},
	language = {en},
	number = {2},
	journal = {Proceedings of the London Mathematical Society},
	author = {Kendall, Wilfrid S.},
	month = sep,
	year = {1990},
	pages = {371--406}
}

@article{karcher_riemannian_1977,
	title = {Riemannian center of mass and mollifier smoothing},
	volume = {30},
	issn = {00103640, 10970312},
	doi = {10.1002/cpa.3160300502},
	language = {en},
	number = {5},
	journal = {Communications on Pure and Applied Mathematics},
	author = {Karcher, H.},
	month = sep,
	year = {1977},
	pages = {509--541}
}

@article{minsker_geometric_2015,
	title = {Geometric median and robust estimation in {Banach} spaces},
	volume = {21},
	issn = {1350-7265},
	doi = {10.3150/14-BEJ645},
	abstract = {In many real-world applications, collected data are contaminated by noise with heavy-tailed distribution and might contain outliers of large magnitude. In this situation, it is necessary to apply methods which produce reliable outcomes even if the input contains corrupted measurements. We describe a general method which allows one to obtain estimators with tight concentration around the true parameter of interest taking values in a Banach space. Suggested construction relies on the fact that the geometric median of a collection of independent "weakly concentrated" estimators satisfies a much stronger deviation bound than each individual element in the collection. Our approach is illustrated through several examples, including sparse linear regression and low-rank matrix recovery problems.},
	number = {4},
	journal = {Bernoulli},
	author = {Minsker, Stanislav},
	month = nov,
	year = {2015},
	note = {arXiv: 1308.1334},
	keywords = {Mathematics - Statistics Theory},
	pages = {2308--2335},
	file = {[Minsker.2015] Geometric median and robust estimation in Banach spaces.pdf:/home/kisung/Dropbox/VP. Packages/RiemBaseExt/[Minsker.2015] Geometric median and robust estimation in Banach spaces.pdf:application/pdf;arXiv.org Snapshot:/home/kisung/Zotero/storage/4DZV78QY/1308.html:text/html}
}

@article{feng_distributed_2015,
	title = {Distributed {Robust} {Learning}},
	url = {http://arxiv.org/abs/1409.5937},
	journal = {arXiv:1409.5937 [cs, stat]},
	author = {Feng, Jiashi and Xu, Huan and Mannor, Shie},
	month = feb,
	year = {2015},
	note = {arXiv: 1409.5937},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
	file = {[Feng.2015] Distributed Robust Learning.pdf:/home/kisung/Dropbox/VP. Packages/RiemBaseExt/[Feng.2015] Distributed Robust Learning.pdf:application/pdf;arXiv.org Snapshot:/home/kisung/Zotero/storage/25QG5IQ4/1409.html:text/html}
}

@article{lerasle_robust_2011,
	title = {Robust empirical mean {Estimators}},
	abstract = {We study robust estimators of the mean of a probability measure \$P\$, called robust empirical mean estimators. This elementary construction is then used to revisit a problem of aggregation and a problem of estimator selection, extending these methods to not necessarily bounded collections of previous estimators. We consider then the problem of robust \$M\$-estimation. We propose a slightly more complicated construction to handle this problem and, as examples of applications, we apply our general approach to least-squares density estimation, to density estimation with K{\textbackslash}"ullback loss and to a non-Gaussian, unbounded, random design and heteroscedastic regression problem. Finally, we show that our strategy can be used when the data are only assumed to be mixing.},
	journal = {arXiv:1112.3914 [math, stat]},
	author = {Lerasle, M. and Oliveira, R. I.},
	month = dec,
	year = {2011},
	note = {arXiv: 1112.3914},
	keywords = {Mathematics - Statistics Theory},
	file = {[Lerasle.2011] Robust empirical mean Estimators.pdf:/home/kisung/Dropbox/VP. Packages/RiemBaseExt/[Lerasle.2011] Robust empirical mean Estimators.pdf:application/pdf;arXiv.org Snapshot:/home/kisung/Zotero/storage/8MM898ND/1112.html:text/html}
}

@article{hahsler_dbscan_2019,
	title = {dbscan : {Fast} {Density}-{Based} {Clustering} with {R}},
	volume = {91},
	issn = {1548-7660},
	doi = {10.18637/jss.v091.i01},
	language = {en},
	number = {1},
	journal = {Journal of Statistical Software},
	author = {Hahsler, Michael and Piekenbrock, Matthew and Doran, Derek},
	year = {2019}
}

@inproceedings{ester_density-based_1996,
	series = {{KDD}'96},
	title = {A {Density}-based {Algorithm} for {Discovering} {Clusters} a {Density}-based {Algorithm} for {Discovering} {Clusters} in {Large} {Spatial} {Databases} with {Noise}},
	booktitle = {Proceedings of the {Second} {International} {Conference} on {Knowledge} {Discovery} and {Data} {Mining}},
	publisher = {AAAI Press},
	author = {Ester, Martin and Kriegel, Hans-Peter and Sander, Jörg and Xu, Xiaowei},
	year = {1996},
	note = {event-place: Portland, Oregon},
	keywords = {arbitrary shape of clusters, clustering algorithms, efficiency on large spatial databases, handling nlj4-275oise},
	pages = {226--231}
}