Electronic Journal of Statistics
- Electron. J. Statist.
- Volume 5 (2011), 1123-1160.
Two proposals for robust PCA using semidefinite programming
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.
Electron. J. Statist., Volume 5 (2011), 1123-1160.
First available in Project Euclid: 15 September 2011
Permanent link to this document
Digital Object Identifier
Mathematical Reviews number (MathSciNet)
Zentralblatt MATH identifier
Primary: 60H25: Random operators and equations [See also 47B80] 62G35: Robustness
Secondary: 90C22: Semidefinite programming
McCoy, Michael; Tropp, Joel A. Two proposals for robust PCA using semidefinite programming. Electron. J. Statist. 5 (2011), 1123--1160. doi:10.1214/11-EJS636. https://projecteuclid.org/euclid.ejs/1316092870