Low-rank approximation based non-negative multi-way array decomposition on event-related potentials

Fengyu Cong, Guoxu Zhou, Piia Astikainen, Qibin Zhao, Qiang Wu, Asoke K. Nandi, Jari K. Hietanen, Tapani Ristaniemi, Andrzej Cichocki

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)

Abstract

Non-negative tensor factorization (NTF) has been successfully applied to analyze event-related potentials (ERPs), and shown superiority in terms of capturing multi-domain features. However, the time-frequency representation of ERPs by higher-order tensors are usually large-scale, which prevents the popularity of most tensor factorization algorithms. To overcome this issue, we introduce a non-negative canonical polyadic decomposition (NCPD) based on low-rank approximation (LRA) and hierarchical alternating least square (HALS) techniques. We applied NCPD (LRAHALS and benchmark HALS) and CPD to extract multi-domain features of a visual ERP. The features and components extracted by LRAHALS NCPD and HALS NCPD were very similar, but LRAHALS NCPD was 70 times faster than HALS NCPD. Moreover, the desired multi-domain feature of the ERP by NCPD showed a significant group difference (control versus depressed participants) and a difference in emotion processing (fearful versus happy faces). This was more satisfactory than that by CPD, which revealed only a group difference.

Original languageEnglish
Article number1440005
JournalInternational Journal of Neural Systems
Volume24
Issue number8
DOIs
Publication statusPublished - 25 Dec 2014
Externally publishedYes

Keywords

  • Event-related potential
  • low-rank approximation
  • multi-domain feature
  • non-negative canonical polyadic decomposition
  • non-negative tensor factorization
  • tensor decomposition

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