On revealing replicating structures in multiway data: A novel tensor decomposition approach

Anh Huy Phan, Andrzej Cichocki, Petr Tichavský, Danilo P. Mandic, Kiyotoshi Matsuoka

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

17 Citations (Scopus)

Abstract

A novel tensor decomposition is proposed to make it possible to identify replicating structures in complex data, such as textures and patterns in music spectrograms. In order to establish a computational framework for this paradigm, we adopt a multiway (tensor) approach. To this end, a novel tensor product is introduced, and the subsequent analysis of its properties shows a perfect match to the task of identification of recurrent structures present in the data. Out of a whole class of possible algorithms, we illuminate those derived so as to cater for orthogonal and nonnegative patterns. Simulations on texture images and a complex music sequence confirm the benefits of the proposed model and of the associated learning algorithms.

Original languageEnglish
Title of host publicationLatent Variable Analysis and Signal Separation - 10th International Conference, LVA/ICA 2012, Proceedings
Pages297-305
Number of pages9
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event10th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2012 - Tel Aviv, Israel
Duration: 12 Mar 201215 Mar 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7191 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2012
Country/TerritoryIsrael
CityTel Aviv
Period12/03/1215/03/12

Keywords

  • nonnegative matrix decomposition
  • pattern analysis
  • structural complexity
  • tensor decomposition
  • tensor product

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