6533b830fe1ef96bd12965f7
RESEARCH PRODUCT
LOW-RANK APPROXIMATION BASED NON-NEGATIVE MULTI-WAY ARRAY DECOMPOSITION ON EVENT-RELATED POTENTIALS
Qiang WuQibin ZhaoFengyu CongFengyu CongAsoke K. NandiAsoke K. NandiTapani RistaniemiAndrzej CichockiAndrzej CichockiPiia AstikainenJari K. HietanenGuoxu Zhousubject
AdultMaleComputer Networks and CommunicationsEmotionsLow-rank approximationEmotional processingEvent-related potentialDecomposition (computer science)Feature (machine learning)HumansRepresentation (mathematics)ta515Mathematicsta113Depressionbusiness.industryGroup (mathematics)ElectroencephalographyPattern recognitionGeneral MedicineMiddle AgedFacial ExpressionAlgebraData Interpretation StatisticalBenchmark (computing)Evoked Potentials VisualFemaleArtificial intelligencebusinessdescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2014-11-20 | International Journal of Neural Systems |