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Selecting discriminative features with discriminative multiple canonical correlationanalysis for multi-feature information fusion

dc.contributor.authorGao, Lei
dc.contributor.authorQi, Lin
dc.contributor.authorGuan, Ling
dc.contributor.editorBrömme, Arslan
dc.contributor.editorBusch, Christoph
dc.date.accessioned2018-10-31T12:34:00Z
dc.date.available2018-10-31T12:34:00Z
dc.date.issued2013
dc.description.abstractIn this paper, it presents a novel approach for selecting discriminative features in multimodal information fusion based discriminative multiple canonical correlation analysis (DMCCA), which is the generalized form of canonical correlation analysis (CCA), multiple canonical correlation analysis (MCCA) and discriminative canonical correlation analysis (DCCA). The proposed approach identifies the discriminative features from the multi-feature in Fractional Fourier Transform (FRFT) domain, which are capable of simultaneously maximizing the within-class correlation and minimizing the between-class correlation, leading to better utilization of the multi-feature information and producing more effective pattern recognition results. The effectiveness of the introduced solution is demonstrated through extensive experimentation on a visual based emotion recognition problem.en
dc.identifier.isbn978-3-88579-606-0
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/17682
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofBIOSIG 2013
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-212
dc.titleSelecting discriminative features with discriminative multiple canonical correlationanalysis for multi-feature information fusionen
dc.typeText/Conference Paper
gi.citation.endPage304
gi.citation.publisherPlaceBonn
gi.citation.startPage297
gi.conference.date04.-06. September 2013
gi.conference.locationDarmstadt
gi.conference.sessiontitleRegular Research Papers

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