Sparse PCA

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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Topic mentions per year

2001-2016
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2014
2014
Sparse Principal Component Analysis (PCA) is a dimensionality reduction technique wherein one seeks a low-rank representation of… (More)
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Highly Cited
2014
Highly Cited
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
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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Highly Cited
2010
Highly Cited
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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Highly Cited
2010
Highly Cited
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
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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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… (More)
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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… (More)
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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… (More)
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