Kernel sparse subspace clustering

Vishal M. Patel, René Vidal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

199 Scopus citations


Subspace clustering refers to the problem of grouping data points that lie in a union of low-dimensional subspaces. One successful approach for solving this problem is sparse subspace clustering, which is based on a sparse representation of the data. In this paper, we extend SSC to non-linear manifolds by using the kernel trick. We show that the alternating direction method of multipliers can be used to efficiently find kernel sparse representations. Various experiments on synthetic as well real datasets show that non-linear mappings lead to sparse representation that give better clustering results than state-of-the-art methods.

Original languageEnglish (US)
Title of host publication2014 IEEE International Conference on Image Processing, ICIP 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)9781479957514
StatePublished - Jan 28 2014

Publication series

Name2014 IEEE International Conference on Image Processing, ICIP 2014

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition


  • Subspace clustering
  • kernel methods
  • non-linear subspace clustering
  • sparse subspace clustering


Dive into the research topics of 'Kernel sparse subspace clustering'. Together they form a unique fingerprint.

Cite this