Abstract

In this paper, we study the k-support norm regularized matrix pur- suit problem, which is regarded as the core formulation for several popular com- puter vision tasks. The k-support matrix norm, a convex relaxation of the matrix sparsity combined with the -norm penalty, generalizes the recently proposed k- support vector norm. The contributions of this work are two-fold. First, the pro- posed k-support matrix norm does not suffer from the disadvantages of existing matrix norms towards sparsity and/or low-rankness: 1) too sparse/dense, and/or 2) column independent. Second, we present an efficient procedure for k-support norm optimization, in which the computation of the key proximity operator is substantially accelerated by binary search. Extensive experiments on subspace segmentation, semi-supervised classi fication and sparse coding well demonstrate the superiority of the new regularizer over existing matrix-norm regularizers, and also the orders-of-magnitude speedup compared with the existing optimization procedure for the k-support norm.