Structured Sparse Coding for Classification via Reweighted $$\ell _{2,1}$$ Minimization

Abstract

In recent years, sparse coding has been used in a wide range of applications including classification and recognition. Different from many other applications, the sparsity pattern of features in many classification tasks are structured and constrained in some feasible domain. In this paper, we proposed a re-weighted $$\ell _{2,1}$$ norm based structured sparse coding method to exploit such structures in the context of classification and recognition. In the proposed method, the dictionary is learned by imposing the class-specific structured sparsity on the sparse codes associated with each category, which can bring noticeable improvement on the discriminability of sparse codes. An alternating iterative algorithm is presented for the proposed sparse coding scheme. We evaluated our method by applying it to several image classification tasks. The experiments showed the improvement of the proposed structured sparse coding method over several existing discriminative sparse coding methods on tested data sets.