TUTCRIS - Tampereen teknillinen yliopisto

TUTCRIS

Enhancing class discrimination in Kernel Discriminant Analysis

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
OtsikkoICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
KustantajaThe Institute of Electrical and Electronics Engineers, Inc.
Sivut1926-1930
Sivumäärä5
ISBN (painettu)9781467369978
DOI - pysyväislinkit
TilaJulkaistu - 4 elokuuta 2015
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
Tapahtuma40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Brisbane, Austraalia
Kesto: 19 huhtikuuta 201424 huhtikuuta 2014

Conference

Conference40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015
MaaAustraalia
KaupunkiBrisbane
Ajanjakso19/04/1424/04/14

Tiivistelmä

In this paper, we propose an optimization scheme aiming at optimal nonlinear data projection, in terms of Fisher ratio maximization. To this end, we formulate an iterative optimization scheme consisting of two processing steps: optimal data projection calculation and optimal class representation determination. Compared to the standard approach employing the class mean vectors for class representation, the proposed optimization scheme increases class discrimination in the reduced-dimensionality feature space. We evaluate the proposed method in standard classification problems, as well as on the classification of human actions and face, and show that it is able to achieve better generalization performance, when compared to the standard approach.