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» Optimal Solutions for Sparse Principal Component Analysis
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CORR
2007
Springer
167views Education» more  CORR 2007»
9 years 3 months ago
Optimal Solutions for Sparse Principal Component Analysis
Given a sample covariance matrix, we examine the problem of maximizing the variance explained by a linear combination of the input variables while constraining the number of nonze...
Alexandre d'Aspremont, Francis R. Bach, Laurent El...
ICML
2007
IEEE
10 years 4 months ago
Full regularization path for sparse principal component analysis
Given a sample covariance matrix, we examine the problem of maximizing the variance explained by a particular linear combination of the input variables while constraining the numb...
Alexandre d'Aspremont, Francis R. Bach, Laurent El...
JMLR
2010
144views more  JMLR 2010»
8 years 10 months ago
Practical Approaches to Principal Component Analysis in the Presence of Missing Values
Principal component analysis (PCA) is a classical data analysis technique that finds linear transformations of data that retain the maximal amount of variance. We study a case whe...
Alexander Ilin, Tapani Raiko
CORR
2010
Springer
130views Education» more  CORR 2010»
9 years 4 months ago
Stable Principal Component Pursuit
In this paper, we study the problem of recovering a low-rank matrix (the principal components) from a highdimensional data matrix despite both small entry-wise noise and gross spar...
Zihan Zhou, Xiaodong Li, John Wright, Emmanuel J. ...
JMLR
2010
155views more  JMLR 2010»
8 years 10 months ago
Structured Sparse Principal Component Analysis
We present an extension of sparse PCA, or sparse dictionary learning, where the sparsity patterns of all dictionary elements are structured and constrained to belong to a prespeci...
Rodolphe Jenatton, Guillaume Obozinski, Francis Ba...
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