Abstract
Recently, sparse coding has received an increasing amount of interests. In this paper, a new algorithm named affine-constrained group sparse coding based on mixed norm (MNACGSC) is presented, which further extends the framework of sparse representation-based classification (SRC). From the perspective of geometry, affine-constrained group sparse coding based on mixed norm (MNACGSC) not only finds out the vector that can be best edcoded according to the given dictionary in the convex hull spanned by input samples, but also establishes on multiple regularization terms which can leverage the collaborative effects of those regularization terms to strength the robustness. This paper mainly discusses \( L_1 \)-norm and \( L_2 \)-norm. The experimental results have demonstrated that the proposed model is effective, robust to noise and outperforms some representative methods.
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