Robust tensor principal component analysis by Lp-norm for image analysis

摘要

Tensor principal component analysis (TPCA), which can make full use of the spatial relationship of images/videos, is a generalization of the classical principal component analysis (PCA). However, the existing TPCA method is based on the Frobenius-norm, which makes it sensitive to outliers. In order to overcome the drawback of TPCA, in this paper, we proposed a novel Lp-norm based TPCA (TPCA-Lp), which is robust to outliers. We also designed an iterative algorithm to solve the optimization of TPCA-Lp, in which all projection matrices are optimized by turns. Experimental results upon several face databases demonstrate the advantages of the proposed approach.