Robust principal component analysis

Known as: Robust PCA 
Robust Principal Component Analysis (RPCA) is a modification of the widely used statistical procedure of principal component analysis (PCA) which… (More)
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2015
2015
Principal Component Analysis (PCA) is the most widely used tool for linear dimensionality reduction and clustering. Still it is… (More)
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Highly Cited
2014
Highly Cited
2014
Principal Component Analysis (PCA) is the most widely used unsupervised dimensionality reduction approach. In recent research… (More)
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Highly Cited
2012
Highly Cited
2012
In this paper, we address the error correction problem, that is, to uncover the low-dimensional subspace structure from high… (More)
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Highly Cited
2011
Highly Cited
2011
This article is about a curious phenomenon. Suppose we have a data matrix, which is the superposition of a low-rank component and… (More)
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Highly Cited
2011
Highly Cited
2011
Principal Component Analysis (PCA) is one of the most important methods to handle highdimensional data. However, the high… (More)
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Highly Cited
2011
Highly Cited
2011
Principal component analysis (PCA) minimizes the mean square error (MSE) and is sensitive to outliers. In this paper, we present… (More)
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Highly Cited
2009
Highly Cited
2009
Principal component analysis is a fundamental operation in computational data analysis, with myriad applications ranging from web… (More)
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Highly Cited
2007
Highly Cited
2007
A method for exploring the structure of populations of complex objects, such as images, is considered. The objects are summaa… (More)
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Highly Cited
2006
Highly Cited
2006
Principal Component Analysis (PCA) is very sensitive in presence of outliers. One of the most appealing robust methods for… (More)
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Highly Cited
2005
Highly Cited
2005
We introduce a new method for robust principal component analysis (PCA). Classical PCA is based on the empirical covariance… (More)
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