PARAFAC-based multilinear subspace clustering for tensor data

Panagiotis A. Traganitis; Georgios B Giannakis

doi:10.1109/GlobalSIP.2016.7906047

PARAFAC-based multilinear subspace clustering for tensor data

Panagiotis A. Traganitis, Georgios B Giannakis

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 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 language	English (US)
Title of host publication	2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1280-1284
Number of pages	5
ISBN (Electronic)	9781509045457
DOIs	https://doi.org/10.1109/GlobalSIP.2016.7906047
State	Published - Apr 19 2017
Event	2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Washington, United States Duration: Dec 7 2016 → Dec 9 2016

Publication series

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

Other

Other	2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016
Country/Territory	United States
City	Washington
Period	12/7/16 → 12/9/16

Bibliographical note

Funding Information:
1Work supported by NSF grants 1500713, 1514056; ARO W911NF-15-1- 0492; and NIH 1R01GM104975-01.

Publisher Copyright:
© 2016 IEEE.

Keywords

Canonical Polyadic Decomposition
PARAFAC
Subspace clustering
Tensor

Publisher link

10.1109/GlobalSIP.2016.7906047

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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). Article 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

PARAFAC-based multilinear subspace clustering for tensor data. / Traganitis, Panagiotis A.; Giannakis, Georgios B.
2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1280-1284 7906047 (2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Traganitis, PA & Giannakis, GB 2017, PARAFAC-based multilinear subspace clustering for tensor data. in 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings., 7906047, 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 1280-1284, 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016, Washington, United States, 12/7/16. https://doi.org/10.1109/GlobalSIP.2016.7906047

Traganitis PA, Giannakis GB. PARAFAC-based multilinear subspace clustering for tensor data. In 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1280-1284. 7906047. (2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings). doi: 10.1109/GlobalSIP.2016.7906047

Traganitis, Panagiotis A. ; Giannakis, Georgios B. / PARAFAC-based multilinear subspace clustering for tensor data. 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 1280-1284 (2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings).

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PARAFAC-based multilinear subspace clustering for tensor data

Abstract

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Bibliographical note

Keywords

Publisher link

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