Online Sparse Subspace Clustering
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2019
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| _version_ | 1866914867862044672 |
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| author | Madden, Liam Becker, Stephen Dall'Anese, Emiliano |
| author_facet | Madden, Liam Becker, Stephen Dall'Anese, Emiliano |
| contents | This paper focuses on the sparse subspace clustering problem, and develops an online algorithmic solution to cluster data points on-the-fly, without revisiting the whole dataset. The strategy involves an online solution of a sparse representation (SR) problem to build a (sparse) dictionary of similarities where points in the same subspace are considered "similar," followed by a spectral clustering based on the obtained similarity matrix. When the SR cost is strongly convex, the online solution converges to within a neighborhood of the optimal time-varying batch solution. A dynamic regret analysis is performed when the SR cost is not strongly convex. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1902_10842 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | Online Sparse Subspace Clustering Madden, Liam Becker, Stephen Dall'Anese, Emiliano Optimization and Control This paper focuses on the sparse subspace clustering problem, and develops an online algorithmic solution to cluster data points on-the-fly, without revisiting the whole dataset. The strategy involves an online solution of a sparse representation (SR) problem to build a (sparse) dictionary of similarities where points in the same subspace are considered "similar," followed by a spectral clustering based on the obtained similarity matrix. When the SR cost is strongly convex, the online solution converges to within a neighborhood of the optimal time-varying batch solution. A dynamic regret analysis is performed when the SR cost is not strongly convex. |
| title | Online Sparse Subspace Clustering |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/1902.10842 |