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Sparse PCA
Sparse principal component analysis (sparse PCA) is a specialised technique used in statistical analysis and, in particular, in the analysis of…
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7 relations
Convex set
List of transforms
Planted clique
Principal component analysis
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Papers overview
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Highly Cited
2015
Highly Cited
2015
Phase transitions in sparse PCA
T. Lesieur
,
F. Krzakala
,
L. Zdeborová
International Symposium on Information Theory
2015
Corpus ID: 2319561
We study optimal estimation for sparse principal component analysis when the number of non-zero elements is small but on the same…
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Highly Cited
2014
Highly Cited
2014
Information-theoretically optimal sparse PCA
Y. Deshpande
,
A. Montanari
IEEE International Symposium on Information…
2014
Corpus ID: 1017938
Sparse Principal Component Analysis (PCA) is a dimensionality reduction technique wherein one seeks a low-rank representation of…
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Highly Cited
2013
Highly Cited
2013
Fantope Projection and Selection: A near-optimal convex relaxation of sparse PCA
Vincent Q. Vu
,
Juhee Cho
,
Jing Lei
,
Karl Rohe
Neural Information Processing Systems
2013
Corpus ID: 15072429
We propose a novel convex relaxation of sparse principal subspace estimation based on the convex hull of rank-d projection…
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Highly Cited
2013
Highly Cited
2013
Sparse PCA via Covariance Thresholding
Y. Deshpande
,
A. Montanari
Journal of machine learning research
2013
Corpus ID: 459883
In sparse principal component analysis we are given noisy observations of a low-rank matrix of dimension n x p and seek to…
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Highly Cited
2012
Highly Cited
2012
Sparse PCA: Optimal rates and adaptive estimation
Tommaso Cai
,
Zongming Ma
,
Yihong Wu
2012
Corpus ID: 10402082
Principal component analysis (PCA) is one of the most commonly used statistical procedures with a wide range of applications…
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Highly Cited
2008
Highly Cited
2008
Deflation Methods for Sparse PCA
Lester W. Mackey
Neural Information Processing Systems
2008
Corpus ID: 325546
In analogy to the PCA setting, the sparse PCA problem is often solved by iteratively alternating between two subtasks…
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Highly Cited
2008
Highly Cited
2008
Expectation-maximization for sparse and non-negative PCA
C. Sigg
,
J. Buhmann
International Conference on Machine Learning
2008
Corpus ID: 503521
We study the problem of finding the dominant eigenvector of the sample covariance matrix, under additional constraints on the…
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Highly Cited
2006
Highly Cited
2006
Nonnegative Sparse PCA
Ron Zass
,
A. Shashua
Neural Information Processing Systems
2006
Corpus ID: 6179957
We describe a nonnegative variant of the "Sparse PCA" problem. The goal is to create a low dimensional representation from a…
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Highly Cited
2005
Highly Cited
2005
Spectral Bounds for Sparse PCA: Exact and Greedy Algorithms
B. Moghaddam
,
Yair Weiss
,
S. Avidan
Neural Information Processing Systems
2005
Corpus ID: 2488916
Sparse PCA seeks approximate sparse "eigenvectors" whose projections capture the maximal variance of data. As a cardinality…
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Highly Cited
2004
Highly Cited
2004
A Direct Formulation for Sparse Pca Using Semidefinite Programming
A. d’Aspremont
,
L. Ghaoui
,
Michael I. Jordan
,
Gert R. G. Lanckriet
SIAM Review
2004
Corpus ID: 5490061
We examine the problem of approximating, in the Frobenius-norm sense, a positive, semidefinite symmetric matrix by a rank-one…
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