Sparse Representation Classification Beyond L1 Minimization and the Subspace Assumption

TL;DR

This paper introduces a new screening-based implementation of the Sparse Representation Classifier (SRC), proving its theoretical equivalence and consistency, while demonstrating faster performance without sacrificing accuracy.

Contribution

It presents a novel screening method for SRC, establishing its theoretical properties and practical efficiency improvements over traditional L1 minimization approaches.

Findings

The screening-based SRC is theoretically equivalent to the original under certain conditions.

The new method achieves similar classification accuracy as traditional SRC.

It significantly reduces computational time in experiments.

Abstract

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.