Linked PARAFAC/CP tensor decomposition and its fast implementation for multi-block tensor analysis

Tatsuya Yokota, Andrzej Cichocki, Yukihiko Yamashita

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

17 Citations (Scopus)

Abstract

In this paper we propose a new flexible group tensor analysis model called the linked CP tensor decomposition (LCPTD). The LCPTD method can decompose given multiple tensors into common factor matrices, individual factor matrices, and core tensors, simultaneously. We applied the Hierarchical Alternating Least Squares (HALS) algorithm to the LCPTD model; besides we impose additional constraints to obtain sparse and nonnegative factors. Furthermore, we conducted some experiments of this model to demonstrate its advantages over existing models.

Original languageEnglish
Title of host publicationNeural Information Processing - 19th International Conference, ICONIP 2012, Proceedings
Pages84-91
Number of pages8
EditionPART 3
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event19th International Conference on Neural Information Processing, ICONIP 2012 - Doha, Qatar
Duration: 12 Nov 201215 Nov 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume7665 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference19th International Conference on Neural Information Processing, ICONIP 2012
Country/TerritoryQatar
CityDoha
Period12/11/1215/11/12

Keywords

  • Group Analysis
  • Hierarchical Alternating Least Squares (HALS)
  • PARAFAC/CP model
  • Tensor decompositions of multi-block data

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