Sparse Subspace Clustering with Sequentially Ordered and Weighted 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 contribution

Abstract

Built on the sparse representation framework, sparse subspace clustering (SSC) received considerable attention in the recent years. Conventional SSC employs ℓ1 -minimization based sparse regression for neighbor identification on a sample-by-sample basis, and is unaware of the neighbor information revealed by those already computed sparse representation vectors. To rid this drawback, this paper proposes a weighted ℓ1 -minimization based sparse regression method, and an associated data ordering rule able to reflect the reliability of neighbor information for further enhancing the clustering accuracy. The selection of weighting coefficients for SSC is also discussed. Computer simulations using both the synthesis and real data are provided to evidence the effectiveness of the proposed method.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings
PublisherIEEE Computer Society
Pages3387-3391
Number of pages5
ISBN (Electronic)9781538662496
DOIs
StatePublished - Sep 2019
Event26th IEEE International Conference on Image Processing, ICIP 2019 - Taipei, Taiwan
Duration: 22 Sep 201925 Sep 2019

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2019-September
ISSN (Print)1522-4880

Conference

Conference26th IEEE International Conference on Image Processing, ICIP 2019
CountryTaiwan
CityTaipei
Period22/09/1925/09/19

Keywords

  • compressive sensing
  • sparse representation
  • Subspace clustering

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  • Cite this

    Wu, J. Y., Huang, L. C., Yang, M. H., Chang, L. H., & Liu, C. H. (2019). Sparse Subspace Clustering with Sequentially Ordered and Weighted L1-Minimization. In 2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings (pp. 3387-3391). [8803440] (Proceedings - International Conference on Image Processing, ICIP; Vol. 2019-September). IEEE Computer Society. https://doi.org/10.1109/ICIP.2019.8803440