Sparse Representation Classification Beyond ℓ1 Minimization and the Subspace Assumption
The sparse representation classifier (SRC) has been utilized in various classification problems, which makes use of <inline-formula> <tex-math notation="LaTeX">$\ell 1$ </tex-math></inline-formula> 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.