title: Robust principal component analysis based on maximum correntropy criterion.
abstract: Principal component analysis (PCA) minimizes the mean square error (MSE) and is sensitive to outliers. In this paper, we present a new rotational-invariant PCA based on maximum correntropy criterion (MCC). A half-quadratic optimization algorithm is adopted to compute the correntropy objective. At each iteration, the complex optimization problem is reduced to a quadratic problem that can be efficiently solved by a standard optimization method. The proposed method exhibits the following benefits: 1) it is robust to outliers through the mechanism of MCC which can be more theoretically solid than a heuristic rule based on MSE; 2) it requires no assumption about the zero-mean of data for processing and can estimate data mean during optimization; and 3) its optimal solution consists of principal eigenvectors of a robust covariance matrix corresponding to the largest eigenvalues. In addition, kernel techniques are further introduced in the proposed method to deal with nonlinearly distributed data. Numerical results demonstrate that the proposed method can outperform robust rotational-invariant PCAs based on L(1) norm when outliers occur.
authors.affiliation: National Laboratory of Pattern Recognition, Institute of Automation Chinese Academy of Sciences, Beijing 100190, China. rhe@nlpr.ia.ac.cn
authors.forename: Ran
authors.initials: R
authors.lastname: He
authors.forename: Bao-Gang
authors.initials: BG
authors.lastname: Hu
authors.forename: Wei-Shi
authors.initials: WS
authors.lastname: Zheng
authors.forename: Xiang-Wei
authors.initials: XW
authors.lastname: Kong
mesh_terms: D000465:Algorithms; D003627:Data Interpretation, Statistical; D019277:Entropy; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity