Back to Search
Start Over
Sparse Representation Classification Beyond ℓ1 Minimization and the Subspace Assumption.
- Source :
-
IEEE Transactions on Information Theory . Aug2020, Vol. 66 Issue 8, p5061-5071. 11p. - Publication Year :
- 2020
-
Abstract
- The sparse representation classifier (SRC) has been utilized in various classification problems, which makes use of $\ell 1$ 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00189448
- Volume :
- 66
- Issue :
- 8
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Information Theory
- Publication Type :
- Academic Journal
- Accession number :
- 144615706
- Full Text :
- https://doi.org/10.1109/TIT.2020.2981309