Sparse Representation Classification Beyond L1 Minimization and the Subspace Assumption
This provides a more efficient implementation for researchers and practitioners using sparse representation classification, though it appears incremental as it focuses on computational improvements rather than fundamental changes.
The authors tackled the computational inefficiency of sparse representation classifiers (SRC) by proposing a new implementation via screening, establishing equivalence to original SRC under regularity conditions and proving classification consistency under a latent subspace model with contamination. The new algorithm achieved comparable numerical performance and significantly faster computation in simulations and real data experiments.
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.