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Sparse PCA
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#	Item
21	Joint Statistics Seminar The Hong Kong University of Science and Technology Principal Component Analysis: Regularization, Supervision, Application, Asymptotics by Add to Reading List Source URL: www.bm.ust.hk Language: English - Date: 2014-11-11 22:31:22 Sparse PCA Dimension reduction Kernel principal component analysis Statistics Multivariate statistics Principal component analysis
22	Graduate Seminar Online Robust PCA or Online Sparse + Low-Rank Matrix Recovery Dr. Namrata Vaswani Associate Professor, Add to Reading List Source URL: www.ece.cmu.edu Language: English - Date: 2015-02-25 02:10:41 Multivariate statistics Singular value decomposition Signal processing Data analysis Principal component analysis Sparse matrix Compressed sensing Subspace Vector space Algebra Mathematics Linear algebra
23	Informative Feature Selection for Object Recognition via Sparse PCA∗ Nikhil Naikal, Allen Y. Yang, and S. Shankar Sastry Department of EECS, University of California, Berkeley {nnaikal, yang, sastry}@eecs.berkeley.edu Add to Reading List Source URL: www.eecs.berkeley.edu Language: English - Date: 2011-06-10 12:51:40 Mathematics Singular value decomposition Data analysis Computer vision Principal component analysis Eigenvalues and eigenvectors Object recognition Bundle adjustment Matrix Statistics Algebra Multivariate statistics
24	DO SEMIDEFINITE RELAXATIONS REALLY SOLVE SPARSE PCA? arXiv:1306.3690v1 [math.ST] 16 JunBy Robert Krauthgamer Add to Reading List Source URL: arxiv.org Language: English - Date: 2013-06-17 20:40:45 Data analysis Singular value decomposition Matrices Covariance and correlation Linear algebra Principal component analysis Multivariate normal distribution Covariance matrix Eigenvalues and eigenvectors Statistics Algebra Mathematics
25	The Annals of Statistics 2013, Vol. 41, No. 3, 1055–1084 DOI: AOS1014 © Institute of Mathematical Statistics, 2013 MINIMAX BOUNDS FOR SPARSE PCA WITH NOISY Add to Reading List Source URL: www.wisdom.weizmann.ac.il Language: English - Date: 2013-06-16 05:23:55 Singular value decomposition Data analysis Matrix theory Covariance and correlation Principal component analysis Eigenvalues and eigenvectors Eigendecomposition of a matrix Covariance matrix Multivariate normal distribution Algebra Statistics Linear algebra
26	1 Recursive Robust PCA or Recursive Sparse Recovery in Large but Structured Noise Chenlu Qiu, Namrata Vaswani and Leslie Hogben Add to Reading List Source URL: arxiv.org Language: English - Date: 2013-10-13 00:34:16 Matrices Matrix theory Singular value decomposition Functional analysis Principal component analysis Projection Eigenvalues and eigenvectors Hermitian matrix Matrix Algebra Linear algebra Mathematics
27	Fantope Projection and Selection: A near-optimal convex relaxation of sparse PCA Vincent Q. Vu The Ohio State University [removed] Add to Reading List Source URL: papers.nips.cc Language: English - Date: 2014-11-25 16:30:06 Matrix theory Singular value decomposition Matrices Data analysis Principal component analysis Eigenvalues and eigenvectors Covariance matrix Matrix Spectral theorem Algebra Linear algebra Mathematics
28	Sparse Regression as a Sparse Eigenvalue Problem Baback Moghaddam Jet Propulsion Laboratory California Institute of Technology [removed] Add to Reading List Source URL: ml.jpl.nasa.gov Language: English - Date: 2008-01-09 21:34:49 Singular value decomposition Multivariate statistics Matrix theory Mathematical optimization Principal component analysis Eigendecomposition of a matrix Eigenvalues and eigenvectors Sparse PCA Matrix Algebra Mathematics Linear algebra
29	Sparse PCA: Concepts, Theory, and Algorithms Jing Lei, Department of Statistics, CMU U. Pitt. Biostat Department Seminar, Nov 7, 2013 Collaborators Vince Q. Vu, Stat. Dept. OSU Add to Reading List Source URL: www.stat.cmu.edu Language: English - Date: 2014-01-10 12:18:09 Multivariate statistics Singular value decomposition Data analysis Covariance and correlation Matrices Principal component analysis Covariance matrix Eigenvalues and eigenvectors Matrix Statistics Algebra Linear algebra
30	Fast Pixel/Part Selection with Sparse Eigenvectors Baback Moghaddam Jet Propulsion Laboratory California Institute of Technology Yair Weiss Add to Reading List Source URL: ml.jpl.nasa.gov Language: English - Date: 2007-11-02 17:25:11 Image processing Singular value decomposition Linear algebra Sparse PCA Thresholding Linear discriminant analysis Segmentation Eigenvalues and eigenvectors Feature selection Statistics Algebra Multivariate statistics

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