Nonnegative PARAFAC2: a flexible coupling approach

Jeremy E. Cohen*, Rasmus Bro

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

28 Citations (Scopus)

Abstract

Modeling variability in tensor decomposition methods is one of the challenges of source separation. One possible solution to account for variations from one data set to another, jointly analysed, is to resort to the PARAFAC2 model. However, so far imposing constraints on the mode with variability has not been possible. In the following manuscript, a relaxation of the PARAFAC2 model is introduced, that allows for imposing nonnegativity constraints on the varying mode. An algorithm to compute the proposed flexible PARAFAC2 model is derived, and its performance is studied on both synthetic and chemometrics data.

Original languageEnglish
Title of host publicationLatent Variable Analysis and Signal Separation : 14th International Conference, LVA/ICA 2018, Proceedings
EditorsYannick Deville, Sharon Gannot, Russell Mason, Mark D. Plumbley, Dominic Ward
Number of pages10
PublisherSpringer
Publication date2018
Pages89-98
ISBN (Print)978-3-319-93763-2
ISBN (Electronic)978-3-319-93764-9
DOIs
Publication statusPublished - 2018
Event14th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2018 - Guildford, United Kingdom
Duration: 2 Jul 20185 Jul 2018

Conference

Conference14th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2018
Country/TerritoryUnited Kingdom
CityGuildford
Period02/07/201805/07/2018
SeriesLecture notes in computer science
Volume10891
ISSN0302-9743

Keywords

  • Flexible coupling
  • Nonnegativity constraints
  • PARAFAC2

Cite this