Sparse representation using nonnegative curds and whey

It has been of great interest to find sparse and/or nonnegative representations in computer vision literature. In this paper we propose a novel method to such a purpose and refer to it as nonnegative curds and whey (NNCW). The NNCW procedure consists of two stages. In the first stage we consider a set of sparse and nonnegative representations of a test image, each of which is a linear combination of the images within a certain class, by solving a set of regression-type nonnegative matrix factorization problems. In the second stage we incorporate these representations into a new sparse and nonnegative representation by using the group nonnegative garrote. This procedure is particularly appropriate for discriminant analysis owing to its supervised and nonnegativity nature in sparsity pursuing. Experiments on several benchmark face databases and Caltech 101 image dataset demonstrate the efficiency and effectiveness of our nonnegative curds and whey method.