Dimensionality reduced ℓ0-sparse subspace clustering

Research output: Contribution to conferencePaperpeer-review

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 languageEnglish (US)
Pages2065-2074
Number of pages10
StatePublished - 2018
Externally publishedYes
Event21st International Conference on Artificial Intelligence and Statistics, AISTATS 2018 - Playa Blanca, Lanzarote, Canary Islands, Spain
Duration: Apr 9 2018Apr 11 2018

Conference

Conference21st International Conference on Artificial Intelligence and Statistics, AISTATS 2018
CountrySpain
CityPlaya Blanca, Lanzarote, Canary Islands
Period4/9/184/11/18

ASJC Scopus subject areas

  • Statistics and Probability
  • Artificial Intelligence

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