0000000000775509

AUTHOR

Qiang Wu

showing 3 related works from this author

LOW-RANK APPROXIMATION BASED NON-NEGATIVE MULTI-WAY ARRAY DECOMPOSITION ON EVENT-RELATED POTENTIALS

2014

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 NCP…

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 intelligencebusinessInternational Journal of Neural Systems
researchProduct

Multi-domain feature extraction for small event-related potentials through nonnegative multi-way array decomposition from low dense array EEG

2013

Non-negative Canonical Polyadic decomposition (NCPD) and non-negative Tucker decomposition (NTD) were compared for extracting the multi-domain feature of visual mismatch negativity (vMMN), a small event-related potential (ERP), for the cognitive research. Since signal-to-noise ratio in vMMN is low, NTD outperformed NCPD. Moreover, we proposed an approach to select the multi-domain feature of an ERP among all extracted features and discussed determination of numbers of extracted components in NCPD and NTD regarding the ERP context.

AdultMaleComputer Networks and CommunicationsFeature extractionEmotionsMismatch negativityContext (language use)Signal-To-Noise RatioSignal-to-noise ratioEvent-related potentialDecomposition (computer science)HumansMathematicsBrain MappingElectronic Data Processingbusiness.industryta111BrainPattern recognitionElectroencephalographyGeneral MedicineMiddle AgedFeature (computer vision)Evoked Potentials VisualFemaleArtificial intelligencebusinessPhotic StimulationTucker decompositionInternational Journal of Neural Systems
researchProduct

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

2014

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…

low-rank approximationEvent-related potentialtensor decompositionnon-negative tensor factorizationmulti-domain featurenon-negative canonical polyadic decomposition
researchProduct