Learning modewise independent components from tensor data using multilinear mixing model

Haiping LU*

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Independent component analysis (ICA) is a popular unsupervised learning method. This paper extends it to multilinear modewise ICA (MMICA) for tensors and explores two architectures in learning and recognition. MMICA models tensor data as mixtures generated from modewise source matrices that encode statistically independent information. Its sources have more compact representations than the sources in ICA. We embed ICA into the multilinear principal component analysis framework to solve for each source matrix alternatively with a few iterations. Then we obtain mixing tensors through regularized inverses of the source matrices. Simulations on synthetic data show that MMICA can estimate hidden sources accurately from structured tensor data. Moreover, in face recognition experiments, it outperforms competing solutions with both architectures.

Original languageEnglish
Title of host publicationMachine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2013, Proceedings
Pages288-303
Number of pages16
EditionPART 2
DOIs
Publication statusPublished - 2013
EventEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2013 - Prague, Czech Republic
Duration: 23 Sep 201327 Sep 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume8189 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2013
Country/TerritoryCzech Republic
CityPrague
Period23/09/1327/09/13

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

User-Defined Keywords

  • independent component analysis
  • mixing model
  • multilinear subspace learning
  • tensor
  • unsupervised learning

Fingerprint

Dive into the research topics of 'Learning modewise independent components from tensor data using multilinear mixing model'. Together they form a unique fingerprint.

Cite this