Abstract
Subspace clustering partitions the data that lie on a union of subspaces. ℓ0-Sparse Subspace Clustering (ℓ0-SSC), which belongs to the subspace clustering methods with sparsity prior, guarantees the correctness of subspace clustering under less restrictive assumptions compared to its ℓ1 counterpart such as Sparse Subspace Clustering (SSC) [1] with demonstrated effectiveness in practice. In this paper, we present Dimensionality Reduced ℓ0-Sparse Subspace Clustering (DR-ℓ0-SSC). DR-ℓ0-SSC first projects the data onto a lower dimensional space by linear transformation, then performs ℓ0-SSC on the dimensionality reduced data. The correctness of DR-ℓ0-SSC in terms of the subspace detection property is proved, therefore DR-ℓ0-SSC recovers the underlying subspace structure in the original data from the dimensionality reduced data. Experimental results demonstrate the effectiveness of DR-ℓ0-SSC.
Original language | English (US) |
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Pages | 2065-2074 |
Number of pages | 10 |
State | Published - 2018 |
Externally published | Yes |
Event | 21st International Conference on Artificial Intelligence and Statistics, AISTATS 2018 - Playa Blanca, Lanzarote, Canary Islands, Spain Duration: Apr 9 2018 → Apr 11 2018 |
Conference
Conference | 21st International Conference on Artificial Intelligence and Statistics, AISTATS 2018 |
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Country/Territory | Spain |
City | Playa Blanca, Lanzarote, Canary Islands |
Period | 4/9/18 → 4/11/18 |
ASJC Scopus subject areas
- Statistics and Probability
- Artificial Intelligence