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.
@article{arxiv.1502.01368,
title = {Sparse Representation Classification Beyond L1 Minimization and the Subspace Assumption},
author = {Cencheng Shen and Li Chen and Yuexiao Dong and Carey E. Priebe},
journal= {arXiv preprint arXiv:1502.01368},
year = {2024}
}