期刊
ELECTRONIC JOURNAL OF STATISTICS
卷 5, 期 -, 页码 1123-1160出版社
INST MATHEMATICAL STATISTICS
DOI: 10.1214/11-EJS636
关键词
Robustness; principal component analysis; semidefinite relaxation; leverage; duality
资金
- ONR [N00014-08-1-0883, N00014-11-1-0025]
- AFOSR [FA9550-09-1-0643]
- Sloan Fellowship
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 observat ions. 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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