PARAFAC-based multilinear subspace clustering for tensor data

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

3 Scopus citations

Abstract

Subspace clustering has become an increasingly popular data analysis and machine learning method, whose main assumption is that data are generated from a union of linear subspaces modeling. While successful in many applications these methods do not take into account the multilinear structure of data such as images and video. Prompted by this observation, the present work introduces a multilinear subspace clustering scheme that exploits the structure of the data, and its performance is evaluated on synthetic and real datasets against state-of-the-art subspace clustering algorithms.

Original languageEnglish (US)
Title of host publication2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1280-1284
Number of pages5
ISBN (Electronic)9781509045457
DOIs
StatePublished - Apr 19 2017
Event2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Washington, United States
Duration: Dec 7 2016Dec 9 2016

Publication series

Name2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings

Other

Other2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016
CountryUnited States
CityWashington
Period12/7/1612/9/16

Keywords

  • Canonical Polyadic Decomposition
  • PARAFAC
  • Subspace clustering
  • Tensor

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    Traganitis, P. A., & Giannakis, G. B. (2017). PARAFAC-based multilinear subspace clustering for tensor data. In 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings (pp. 1280-1284). [7906047] (2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GlobalSIP.2016.7906047