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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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Papers overview

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Highly Cited
2015
Highly Cited
2015
We study optimal estimation for sparse principal component analysis when the number of non-zero elements is small but on the same… 
Highly Cited
2014
Highly Cited
2014
Sparse Principal Component Analysis (PCA) is a dimensionality reduction technique wherein one seeks a low-rank representation of… 
Highly Cited
2013
Highly Cited
2013
We propose a novel convex relaxation of sparse principal subspace estimation based on the convex hull of rank-d projection… 
Highly Cited
2013
Highly Cited
2013
In sparse principal component analysis we are given noisy observations of a low-rank matrix of dimension n x p and seek to… 
Highly Cited
2012
Highly Cited
2012
Principal component analysis (PCA) is one of the most commonly used statistical procedures with a wide range of applications… 
Highly Cited
2008
Highly Cited
2008
In analogy to the PCA setting, the sparse PCA problem is often solved by iteratively alternating between two subtasks… 
Highly Cited
2008
Highly Cited
2008
We study the problem of finding the dominant eigenvector of the sample covariance matrix, under additional constraints on the… 
Highly Cited
2006
Highly Cited
2006
We describe a nonnegative variant of the "Sparse PCA" problem. The goal is to create a low dimensional representation from a… 
Highly Cited
2005
Highly Cited
2005
Sparse PCA seeks approximate sparse "eigenvectors" whose projections capture the maximal variance of data. As a cardinality… 
Highly Cited
2004
Highly Cited
2004
We examine the problem of approximating, in the Frobenius-norm sense, a positive, semidefinite symmetric matrix by a rank-one…