Bregman divergence

Results: 39



#Item
1Statistics / Geometry / Statistical distance / Information / Statistical theory / Information theory / Bregman divergence / Search algorithms / KullbackLeibler divergence / Variational Bayesian methods / Expectationmaximization algorithm / Divergence

That was fast! Speeding up NN search of high dimensional distributions. Emanuele Coviello University of California, San Diego, 9500 Gilman Dr, La Jolla, CA 92093

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Source URL: eceweb.ucsd.edu

Language: English - Date: 2015-07-31 19:02:39
2Statistics / Geometry / Statistical distance / Estimation theory / Statistical theory / Bregman divergence / Divergence / Expectationmaximization algorithm / Exponential family / KullbackLeibler divergence / Variational Bayesian methods

That was fast! Speeding up NN search of high dimensional distributions.

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Source URL: eceweb.ucsd.edu

Language: English - Date: 2015-07-31 19:00:25
3Statistical distance / Convex analysis / Probability distributions / Mathematical optimization / Bregman divergence / Normal distribution / Divergence / KullbackLeibler divergence / Convex function / Gamma distribution / Convex conjugate / Symbol

c 2007 Society for Industrial and Applied Mathematics  SIAM J. MATRIX ANAL. APPL. Vol. 29, No. 4, pp. 1120–1146

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Source URL: users.cms.caltech.edu

Language: English - Date: 2008-02-13 00:22:37
4Geometry / Mathematical analysis / Statistical distance / Statistics / F-divergence / KullbackLeibler divergence / Divergence / Bregman divergence / Distribution / Hellinger distance

Nonparametric estimation of the likelihood ratio and divergence functionals 1 XuanLong Nguyen1 , Martin J. Wainwright1,2 and Michael I. Jordan1,2

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Source URL: dept.stat.lsa.umich.edu

Language: English - Date: 2007-05-25 17:26:02
5Mathematical analysis / Mathematical optimization / Mathematics / Convex analysis / Convex function / Bregman divergence / Convex conjugate / Lp space / Convex optimization / Online machine learning / Norm / Multiple kernel learning

Journal of Machine Learning Research1890 Submitted 12/10; Revised 10/11; Published 6/12 Regularization Techniques for Learning with Matrices Sham M. Kakade

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Source URL: dept.stat.lsa.umich.edu

Language: English - Date: 2012-09-12 18:50:25
6Multivariate statistics / Dimension reduction / Matrix theory / Computational statistics / Linear algebra / Nonlinear dimensionality reduction / Bregman divergence / Semidefinite embedding / Principal component analysis / Statistics / Algebra / Mathematics

Regularizers versus Losses for Nonlinear Dimensionality Reduction Yaoliang Yu, James Neufeld, Ryan Kiros, Xinhua Zhang, Dale Schuurmans Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8 Canada

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Source URL: www.cs.toronto.edu

Language: English - Date: 2013-12-26 22:59:57
7Statistical theory / Information theory / Bregman divergence / Kullback–Leibler divergence / Normal distribution / Information geometry / Divergence / Exponential family / Lp space / Statistics / Geometry / Mathematical analysis

CCCG 2006, Kingston, Ontario, August 14–16, 2006 On the Smallest Enclosing Information Disk∗ Frank Nielsen† Abstract

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Source URL: cccg.ca

Language: English - Date: 2008-10-27 22:58:55
8Bregman divergence / Mixture model / Unsupervised learning / Pattern recognition / Supervised learning / Information geometry / Cluster analysis / Support vector machine / Transduction / Statistics / Machine learning / Semi-supervised learning

Generalized Optimal Reverse Prediction Martha White and Dale Schuurmans Department of Computing Science University of Alberta {whitem, dale}@cs.ualberta.ca

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Source URL: webdocs.cs.ualberta.ca

Language: English - Date: 2012-09-28 23:24:08
9Mathematical optimization / Kullback–Leibler divergence / Bregman divergence / Statistics / Convex analysis / Convex optimization

Convex Relaxations of Bregman Divergence Clustering Hao Cheng Department of Computing Science Xinhua Zhang

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Source URL: webdocs.cs.ualberta.ca

Language: English - Date: 2013-07-27 14:43:51
10Multivariate statistics / Dimension reduction / Matrix theory / Computational statistics / Linear algebra / Nonlinear dimensionality reduction / Bregman divergence / Semidefinite embedding / Principal component analysis / Statistics / Algebra / Mathematics

Regularizers versus Losses for Nonlinear Dimensionality Reduction Yaoliang Yu, James Neufeld, Ryan Kiros, Xinhua Zhang, Dale Schuurmans Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8 Canada

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Source URL: webdocs.cs.ualberta.ca

Language: English - Date: 2012-09-28 23:24:23
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