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TUTCRIS

On the optimal class representation in linear discriminant analysis

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
Sivut1491-1497
Sivumäärä7
JulkaisuIEEE Transactions on Neural Networks and Learning Systems
Vuosikerta24
Numero9
DOI - pysyväislinkit
TilaJulkaistu - 2013
OKM-julkaisutyyppiA1 Alkuperäisartikkeli

Tiivistelmä

Linear discriminant analysis (LDA) is a widely used technique for supervised feature extraction and dimensionality reduction. LDA determines an optimal discriminant space for linear data projection based on certain assumptions, e.g., on using normal distributions for each class and employing class representation by the mean class vectors. However, there might be other vectors that can represent each class, to increase class discrimination. In this brief, we propose an optimization scheme aiming at the optimal class representation, in terms of Fisher ratio maximization, for LDA-based data projection. Compared with the standard LDA approach, the proposed optimization scheme increases class discrimination in the reduced dimensionality space and achieves higher classification rates in publicly available data sets.