A1 Journal article (refereed)
Low-rank approximation based non-negative multi-way array decomposition on event-related potentials (2014)


Cong, F., Zhou, G., Astikainen, P., Zhao, Q., Wu, Q., Nandi, A., Hietanen, J. K., Ristaniemi, T., & Cichocki, A. (2014). Low-rank approximation based non-negative multi-way array decomposition on event-related potentials. International Journal of Neural Systems, 24(8), Article 1440005. https://doi.org/10.1142/S012906571440005X


JYU authors or editors


Publication details

All authors or editorsCong, Fengyu; Zhou, Guoxu; Astikainen, Piia; Zhao, Qibin; Wu, Qiang; Nandi, Asoke; Hietanen, Jari K.; Ristaniemi, Tapani; Cichocki, Andrzej

Journal or seriesInternational Journal of Neural Systems

ISSN0129-0657

eISSN1793-6462

Publication year2014

Volume24

Issue number8

Article number1440005

PublisherWorld Scientific

Place of PublicationSingapore

Publication countrySingapore

Publication languageEnglish

DOIhttps://doi.org/10.1142/S012906571440005X

Publication open accessOpenly available

Publication channel open accessPartially open access channel

Publication is parallel published (JYX)https://jyx.jyu.fi/handle/123456789/77841


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.


Free keywordsEvent-related potential; low-rank approximation; multi-domain feature; non-negative canonical polyadic decomposition; non-negative tensor factorization; tensor decomposition


Contributing organizations


Ministry reportingYes

Reporting Year2014

JUFO rating1


Last updated on 2024-09-05 at 22:46