Improved low rank representation : kernelization, efficient optimization and applications
Given data sampled from multiple subspaces, the goal of subspace clustering is to partition data into several clusters, so that each cluster exactly corresponds to one subspace. Initially proposed for subspace clustering, the low rank representation (LRR) approach has shown promising results in vari...
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| Format: | Thesis |
| Language: | English |
| Published: |
2016
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| Online Access: | http://hdl.handle.net/10356/66234 |