Representative class vector clustering-based discriminant analysis

Alexandros Iosifidis, Anastasios Tefas, Ioannis Pitas

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

    3 Citations (Scopus)

    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.

    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 conference proceedings
    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
    Country/TerritoryChina
    CityBeijing
    Period16/10/1318/10/13

    Keywords

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

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Information Systems
    • Signal Processing

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