Genetic Generalized Discriminant Analysis and Its Applications

Lijun Yan, Lin-Lin Tang, Shu-Chuan Chu, Xiaorui Zhu, Jun-Bao Li, Xiaochuan Guo

    Research output: Contribution to conferencePaperpeer-review

    1 Citation (Scopus)

    Abstract

    In this paper, a novel Genetic Generalized Discriminant Analysis (GGDA) is proposed. GGDA is a generalized version of Exponential Discriminant Analysis (EDA). EDA algorithm is equivalent to map the samples to a new space and then perform LDA. However, is this space is optimal for classification? The proposed GGDA uses Genetic Algorithm to search for an more discriminant diffusing map and then perform LDA in the new space. The Experimental results confirm the efficiency of the proposed algorithm.

    Original languageEnglish
    Pages246-255
    Number of pages10
    DOIs
    Publication statusPublished - 1 Jan 2014
    EventThe 27th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2014 -
    Duration: 3 Jun 2014 → …

    Conference

    ConferenceThe 27th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2014
    Period3/06/14 → …

    Keywords

    • Exponential discriminant analysis
    • Feature extraction
    • Genetic algorithm

    Fingerprint Dive into the research topics of 'Genetic Generalized Discriminant Analysis and Its Applications'. Together they form a unique fingerprint.

    Cite this