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On the optimal class representation in linear discriminant analysis

Research output: Contribution to journalArticleScientificpeer-review


Original languageEnglish
Pages (from-to)1491-1497
Number of pages7
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number9
Publication statusPublished - 2013
Publication typeA1 Journal article-refereed


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.


  • Class representation, data projection, linear discriminant analysis (LDA), subspace learning