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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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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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