Work

Sparse Representation Classification Beyond ℓ1 Minimization and the Subspace Assumption

Year: 2020

Type: article

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.

Source: IEEE Transactions on Information Theory

DOI: 10.1109/tit.2020.2981309

Authors: Cencheng Shen, Li Chen, Yuexiao Dong, Carey E. Priebe

Institutions: University of Delaware, Intel (United States), Temple University, Johns Hopkins University

Language: English

Topic: Sparse and Compressive Sensing Techniques

Subfield: Computational Mechanics

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funders: Johns Hopkins University, Defense Advanced Research Projects Agency, Division of Mathematical Sciences

Awards: G3047420522, G5260592332, G5428660764