Sparse Representation Classification Beyond L1 Minimization and the Subspace Assumption

4 Feb 2015  ·  Cencheng Shen, Li Chen, Yuexiao Dong, Carey E. Priebe ·

The sparse representation classifier (SRC) has been utilized in various classification problems, which makes use of L1 minimization and works well for image recognition satisfying a subspace assumption. In this paper we propose a new implementation of SRC via screening, establish its equivalence to the original SRC under regularity conditions, and prove its classification consistency under a latent subspace model and contamination. The results are demonstrated via simulations and real data experiments, where the new algorithm achieves comparable numerical performance and significantly faster.

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