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Title: Low-rank approximation based non-negative multi-way array decomposition on event-related potentials
Authors: Cong, F
Zhou, G
Astikainen, P
Zhao, Q
Wu, Q
Nandi, AK
Hietanen, JK
Ristaniemi, T
Cichocki, A
Keywords: event-related potential;low-rank approximation;multi-domain feature;non-negative canonical polyadic decomposition;non-negative tensor factorization;tensor decomposition
Issue Date: 20-Nov-2014
Publisher: World Scientific Publishing
Citation: Cong, F. et al. (2014) 'Low-rank approximation based non-negative multi-way array decomposition on event-related potentials', International Journal of Neural Systems, 24 (08), 1440005, pp. 1 - 19. doi: 10.1142/S012906571440005X.
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. © 2014 World Scientific Publishing Company.
ISSN: 0129-0657
Other Identifiers: ORCiD: Asoke K. Nandi
Appears in Collections:Dept of Electronic and Electrical Engineering Research Papers

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