TUTCRIS - Tampereen teknillinen yliopisto

TUTCRIS

Representative class vector clustering-based discriminant analysis

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
OtsikkoProceedings - 2013 9th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2013
KustantajaIEEE COMPUTER SOCIETY PRESS
Sivut526-529
Sivumäärä4
ISBN (painettu)9780769551203
DOI - pysyväislinkit
TilaJulkaistu - 2013
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
Tapahtuma9th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2013 - Beijing, Kiina
Kesto: 16 lokakuuta 201318 lokakuuta 2013

Conference

Conference9th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2013
MaaKiina
KaupunkiBeijing
Ajanjakso16/10/1318/10/13

Tiivistelmä

Clustering-based Discriminant Analysis (CDA) is a well-known technique for supervised feature extraction and dimensionality reduction. CDA determines an optimal discriminant subspace for linear data projection based on the assumptions of normal subclass distributions and subclass representation by using the mean subclass vector. However, in several cases, there might be other subclass representative vectors that could be more discriminative, compared to the mean subclass vectors. In this paper we propose an optimization scheme aiming at determining the optimal subclass representation for CDA-based data projection. The proposed optimization scheme has been evaluated on standard classification problems, as well as on two publicly available human action recognition databases providing enhanced class discrimination, compared to the standard CDA approach.