An augmented Lagrangian algorithm for decomposition of symmetric tensors of order-4

Anh Huy Phan, Masao Yamagishi, Andrzej Cichocki

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

1 Citation (Scopus)

Abstract

Decomposition of symmetric tensors has found numerous applications in blind sources separation, blind identification, clustering, and analysis of social interactions. In this paper, we consider fourth order symmetric tensors, and its symmetric tensor decomposition. By imposing unit-length constraints on components, we resort the optimisation problem to the constrained eigenvalue decomposition in which eigenvectors are represented in form of rank-1 matrices. To this end, we develop an augmented Lagrangian algorithm with simple update rules. The proposed algorithm has been compared with the Trust-Region solver over manifold, and achieved higher success rates. The algorithm is also validated for blind identification, and achieves more stable results than the ALSCAF algorithm.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2547-2551
Number of pages5
ISBN (Electronic)9781509041176
DOIs
Publication statusPublished - 16 Jun 2017
Externally publishedYes
Event2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - New Orleans, United States
Duration: 5 Mar 20179 Mar 2017

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
Country/TerritoryUnited States
CityNew Orleans
Period5/03/179/03/17

Keywords

  • augmented Lagrangian algorithm
  • spherical quadratic programming
  • symmetric tensor decomposition

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