Affinity learning via a diffusion process for subspace clustering
Subspace clustering refers to the problem of finding low-dimensional subspaces (clusters) for high-dimensional data. Current state-of-the-art subspace clustering methods are usually based on spectral clustering, where an affinity matrix is learned by the self-expressive model, i.e., reconstructing e...
| Main Authors: | , , |
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| Format: | Journal Article |
| Published: |
Elsevier
2018
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| Online Access: | http://hdl.handle.net/20.500.11937/69801 |