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Representative class vector clustering-based discriminant analysis

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Details

Original languageEnglish
Title of host publicationProceedings - 2013 9th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2013
PublisherIEEE COMPUTER SOCIETY PRESS
Pages526-529
Number of pages4
ISBN (Print)9780769551203
DOIs
Publication statusPublished - 2013
Publication typeA4 Article in a conference publication
Event9th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2013 - Beijing, China
Duration: 16 Oct 201318 Oct 2013

Conference

Conference9th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2013
CountryChina
CityBeijing
Period16/10/1318/10/13

Abstract

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.

Keywords

  • class representation, data projection, Discriminant Analysis, feature selection