IEEE Trans Image Process - Robust principal component analysis based on maximum correntropy criterion.

Tópicos

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Resumo

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.

Resumo Limpo

princip compon analysi pca minim mean squar error mse sensit outlier paper present new rotationalinvari pca base maximum correntropi criterion mcc halfquadrat optim algorithm adopt comput correntropi object iter complex optim problem reduc quadrat problem can effici solv standard optim method propos method exhibit follow benefit robust outlier mechan mcc can theoret solid heurist rule base mse requir assumpt zeromean data process can estim data mean optim optim solut consist princip eigenvector robust covari matrix correspond largest eigenvalu addit kernel techniqu introduc propos method deal nonlinear distribut data numer result demonstr propos method can outperform robust rotationalinvari pcas base l norm outlier occur

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