Sparse coding has shown its great potential in learning image feature representation. Recent developed methods such as group sparse coding prefer discovering the group relationships among examples and have achieved the state-of-the-art results in image classification. However, they suffer from poor robustness shortcomings in practice. This paper proposes a robust weighted supervised sparse coding method (RWSSC) to address this deficiency. Particularly, RWSSC distinguishes different classes' contributions to the sparse coding by a novel weighting strategy meanwhile removes the out liers by imposing l1-regularization over the noisy entries. Benefitting from these strategies, RWSSC can effectively boost performance of sparse coding in image classification. Besides, we developed the block coordinate descent algorithm to optimize it, and proved its convergence. Experimental results of image classification on two popular datasets show that RWSSC outperforms the representative sparse coding methods in quantities.
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