Feature Selection Embedded Subspace Clustering

Chong Peng, Zhao Kang, Ming Yang, Qiang Cheng

Research output: Contribution to journalArticlepeer-review

43 Scopus citations


We propose a new subspace clustering method that integrates feature selection into subspace clustering. Rather than using all features to construct a low-rank representation of the data, we find such a representation using only relevant features, which helps in revealing more accurate data relationships. Two variants are proposed by using both convex and nonconvex rank approximations. Extensive experimental results confirm the effectiveness of the proposed method and models.

Original languageEnglish
Article number7479529
Pages (from-to)1018-1022
Number of pages5
JournalIEEE Signal Processing Letters
Issue number7
StatePublished - Jul 2016

Bibliographical note

Funding Information:
Manuscript received April 20, 2016; revisedMay 16, 2016; accepted May 16, 2016. Date of publication May 26, 2016; date of current version June 23, 2016. This work was supported by NSF under Grant IIS-1218712. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Marco Duarte.

Publisher Copyright:
© 1994-2012 IEEE.

ASJC Scopus subject areas

  • Signal Processing
  • Applied Mathematics
  • Electrical and Electronic Engineering


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