Sparse principal component analysis (sparse PCA) is a specialised technique used in statistical analysis and, in particular, in the analysis of… (More)

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2014

2014

- Yash Deshpande, Andrea Montanari
- 2014 IEEE International Symposium on Information…
- 2014

Sparse Principal Component Analysis (PCA) is a dimensionality reduction technique wherein one seeks a low-rank representation of… (More)

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2014

Highly Cited

2014

- T. Tony Cai, Zongming Ma, Yihong Wu
- 2014

Principal component analysis (PCA) is one of the most commonly used statistical procedures with a wide range of applications… (More)

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2013

2013

We introduce a novel algorithm that computes the k-sparse principal component of a positive semidefinite matrix A. Our algorithm… (More)

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2013

2013

- Quentin Berthet, Philippe Rigollet
- ArXiv
- 2013

In the context of sparse principal component detection, we bring evidence towards the existence of a statistical price to pay for… (More)

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2010

Highly Cited

2010

- Michel Journée, Yurii Nesterov, Peter Richtárik, Rodolphe Sepulchre
- Journal of Machine Learning Research
- 2010

In this paper we develop a new approach to sparse principal component analysis (sparse PCA). We propose two single-unit and two… (More)

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2010

Highly Cited

2010

- Matthias Hein, Thomas Bühler
- NIPS
- 2010

Many problems in machine learning and statistics can be formulated as (generalized) eigenproblems. In terms of the associated… (More)

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2010

2010

- Youwei Zhang, Alexandre d’Aspremont, Laurent El Ghaoui
- 2010

Given a sample covariance matrix, we examine the problem of maximizing the variance explained by a linear combination of the… (More)

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2008

Highly Cited

2008

- Lester W. Mackey
- NIPS
- 2008

In analogy to the PCA setting, the sparse PCA problem is often solved by iteratively alternating between two subtasks… (More)

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2006

Highly Cited

2006

- Ron Zass, Amnon Shashua
- NIPS
- 2006

We describe a nonnegative variant of the ”Sparse PCA” problem. The goal is to create a low dimensional representation from a… (More)

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2005

Highly Cited

2005

- Baback Moghaddam, Yair Weiss, Shai Avidan
- NIPS
- 2005

Sparse PCA seeks approximate sparse “eigenvectors” whose projections capture the maximal variance of data. As a cardinality… (More)

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