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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… Expand
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Papers overview

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2015
2015
We study optimal estimation for sparse principal component analysis when the number of non-zero elements is small but on the same… Expand
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Highly Cited
2014
Highly Cited
2014
Sparse Principal Component Analysis (PCA) is a dimensionality reduction technique wherein one seeks a low-rank representation of… Expand
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Highly Cited
2013
Highly Cited
2013
Principal component analysis (PCA) is one of the most commonly used statistical procedures with a wide range of applications… Expand
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Highly Cited
2010
Highly Cited
2010
Many problems in machine learning and statistics can be formulated as (generalized) eigenproblems. In terms of the associated… Expand
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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… Expand
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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… Expand
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Highly Cited
2008
Highly Cited
2008
Principal component analysis (PCA) is a widely used tool for data analysis and dimension reduction in applications throughout… Expand
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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… Expand
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Highly Cited
2005
Highly Cited
2005
Sparse PCA seeks approximate sparse "eigenvectors" whose projections capture the maximal variance of data. As a cardinality… Expand
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Highly Cited
2004
Highly Cited
2004
Given a covariance matrix, we consider the problem of maximizing the variance explained by a particular linear combination of the… Expand
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