Sparse Representation Classification Beyond L1 Minimization and the Subspace Assumption
Fuente:
arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2015
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| _version_ | 1866909231242805248 |
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| author | Shen, Cencheng Chen, Li Dong, Yuexiao Priebe, Carey E. |
| author_facet | Shen, Cencheng Chen, Li Dong, Yuexiao Priebe, Carey E. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1502_01368 |
| institution | arXiv |
| publishDate | 2015 |
| record_format | arxiv |
| spellingShingle | Sparse Representation Classification Beyond L1 Minimization and the Subspace Assumption Shen, Cencheng Chen, Li Dong, Yuexiao Priebe, Carey E. Machine Learning 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. |
| title | Sparse Representation Classification Beyond L1 Minimization and the Subspace Assumption |
| topic | Machine Learning |
| url | https://arxiv.org/abs/1502.01368 |