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 mini- mization problem with an added lq regularization is solved by alternating direc- tion 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.
['Lei Luo', 'Jian Yang', 'Jinhui Chen', 'Yicheng Gao']
Schatten p-Norm Based Matrix Regression Model for Image Classification