FADA: An efficient dimension reduction scheme for image classification

Yijuan Lu, Jingsheng Ma, Qi Tian

    Research output: Chapter in Book/Report/Conference proceedingConference contribution


    This paper develops a novel and efficient dimension reduction scheme-Fast Adaptive Discriminant Analysis (FADA). FADA can find a good projection with adaptation to different sample distributions and discover the classification in the subspace with naïve Bayes classifier. FADA overcomes the high computational cost problem of current Adaptive Discriminant Analysis (ADA) and also alleviates the overfitting problem implicitly caused by ADA. FADA is tested and evaluated using synthetic dataset, COREL dataset and three different face datasets. The experimental results show FADA is more effective and computationally more efficient than ADA for image classification. © Springer-Verlag Berlin Heidelberg 2007.

    Original languageEnglish
    Title of host publicationAdvances in Multimedia Information Processing - PCM 2007 - 8th Pacific Rim Conference on Multimedia, Proceedings
    Number of pages9
    Volume4810 LNCS
    Publication statusPublished - 2007
    Event8th Pacific-Rim Conference on Multimedia - Hong Kong, Hong Kong
    Duration: 11 Dec 200714 Dec 2007

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume4810 LNCS
    ISSN (Print)0302-9743


    Conference8th Pacific-Rim Conference on Multimedia
    Abbreviated titlePCM 2007
    Country/TerritoryHong Kong
    CityHong Kong


    • Adaptive discriminant analysis
    • Image classification


    Dive into the research topics of 'FADA: An efficient dimension reduction scheme for image classification'. Together they form a unique fingerprint.

    Cite this