Schatten p-Norm Based Matrix Regression Model for Image Classification

Lei Luo, Jian Yang, Jinhui Chen, Yicheng Gao

Abstract: Nuclear norm minimization problems for finding the minimum rank matrix have been well studied in many areas. Schatten p-norm is an extension of nuclear norm and the rank function. Different p provides flexible choices for suiting for different applications. Differing from the viewpoint of rank, we will use Schatten p-norm to characterize the error matrix between the occluded face image and its ground truth. Thus, a Schatten p-norm based matrix regression model is presented and a general framework for solving Schatten p-norm minimization problem with an added l_q regularization is solved by alternating direction method of multipliers (ADMM). The experiments for image classification and face reconstruction show that our algorithm is more effective and efficient, and thus can act as a fast solver for matrix regression problem.