Latent Space Sparse and Low-Rank Subspace Clustering
We propose three novel algorithms for simultaneous dimensionality reduction and clustering of data lying in a union of subspaces. Specifically, we describe methods that learn the projection of data and find the sparse and/or low-rank coefficients in the low-dimensional latent space. Cluster labels a...
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| Veröffentlicht in: | IEEE journal of selected topics in signal processing 2015-06, Vol.9 (4), p.691-701 |
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| container_end_page | 701 |
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| container_issue | 4 |
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| container_title | IEEE journal of selected topics in signal processing |
| container_volume | 9 |
| creator | Patel, Vishal M. Van Nguyen, Hien Vidal, Rene |
| description | We propose three novel algorithms for simultaneous dimensionality reduction and clustering of data lying in a union of subspaces. Specifically, we describe methods that learn the projection of data and find the sparse and/or low-rank coefficients in the low-dimensional latent space. Cluster labels are then assigned by applying spectral clustering to a similarity matrix built from these representations. Efficient optimization methods are proposed and their non-linear extensions based on kernel methods are presented. Various experiments show that the proposed methods perform better than many competitive subspace clustering methods. |
| doi_str_mv | 10.1109/JSTSP.2015.2402643 |
| format | Article |
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| ispartof | IEEE journal of selected topics in signal processing, 2015-06, Vol.9 (4), p.691-701 |
| issn | 1932-4553 1941-0484 |
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| subjects | Clustering algorithms Clustering methods Cost function Dimension reduction Kernel kernel methods low-rank subspace clustering manifold clustering Signal processing algorithms Sparse matrices sparse subspace clustering subspace clustering |
| title | Latent Space Sparse and Low-Rank Subspace Clustering |
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