Enhanced noisy sparse subspace clustering via reweighted L1-Minimization

Jwo-Yuh Wu, Liang Chi Huang, Ming Hsun Yang, Ling Hua Chang, Chun Hung Liu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

Sparse subspace clustering (SSC) relies on sparse regression for accurate neighbor identification. Inspired by recent progress in compressive sensing (CS), this paper proposes a new sparse regression scheme for SSC via reweighted 1-minimization, which also generalizes a two-step 1-minimization algorithm introduced by E. J. Candès al all in [The Annals of Statistics, vol. 42, no. 2, pp. 669-699, 2014] without incurring extra complexity burden. To fully exploit the prior information conveyed by the computed sparse vector in the first step, our approach places a weight on each component of the regression vector, and solves a weighted LASSO in the second step. We discuss the impact of weighting on neighbor identification, argue that a popular weighting rule used in CS literature is not suitable for the SSC purpose, and propose a new weighting scheme for enhancing neighbor identification accuracy. Extensive simulation results are provided to validate our discussions and evidence the effectiveness of the proposed approach. Some key issues for future works are also highlighted.

Original languageEnglish
Title of host publication2018 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2018 - Proceedings
EditorsNelly Pustelnik, Zheng-Hua Tan, Zhanyu Ma, Jan Larsen
PublisherIEEE Computer Society
ISBN (Electronic)9781538654774
DOIs
StatePublished - 31 Oct 2018
Event28th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2018 - Aalborg, Denmark
Duration: 17 Sep 201820 Sep 2018

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing, MLSP
Volume2018-September
ISSN (Print)2161-0363
ISSN (Electronic)2161-0371

Conference

Conference28th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2018
CountryDenmark
CityAalborg
Period17/09/1820/09/18

Keywords

  • Compressive sensing
  • Sparse representation
  • Subspace clustering

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