Open Access
2011 Two proposals for robust PCA using semidefinite programming
Michael McCoy, Joel A. Tropp
Electron. J. Statist. 5: 1123-1160 (2011). DOI: 10.1214/11-EJS636


The performance of principal component analysis suffers badly in the presence of outliers. This paper proposes two novel approaches for robust principal component analysis based on semidefinite programming. The first method, maximum mean absolute deviation rounding, seeks directions of large spread in the data while damping the effect of outliers. The second method produces a low-leverage decomposition of the data that attempts to form a low-rank model for the data by separating out corrupted observations. This paper also presents efficient computational methods for solving these semidefinite programs. Numerical experiments confirm the value of these new techniques.


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Michael McCoy. Joel A. Tropp. "Two proposals for robust PCA using semidefinite programming." Electron. J. Statist. 5 1123 - 1160, 2011.


Published: 2011
First available in Project Euclid: 15 September 2011

zbMATH: 1329.62276
MathSciNet: MR2836771
Digital Object Identifier: 10.1214/11-EJS636

Primary: 60H25 , 62G35
Secondary: 90C22

Keywords: Duality , leverage , Principal Component Analysis , robustness , semidefinite relaxation

Rights: Copyright © 2011 The Institute of Mathematical Statistics and the Bernoulli Society

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