Non-iterative symmetric two-dimensional linear discriminant analysis

Kohei Inoue, Kenji Hara, Kiichi Urahama

    Research output: Contribution to journalArticlepeer-review

    3 Citations (Scopus)


    Linear discriminant analysis (LDA) is one of the wellknown schemes for feature extraction and dimensionality reduction of labeled data. Recently, two-dimensional LDA (2DLDA) for matrices such as images has been reformulated into symmetric 2DLDA (S2DLDA), which is solved by an iterative algorithm. In this paper, we propose a non-iterative S2DLDA and experimentally show that the proposed method achieves comparable classification accuracy with the conventional S2DLDA, while the proposed method is computationally more efficient than the conventional S2DLDA.

    Original languageEnglish
    Pages (from-to)926-929
    Number of pages4
    JournalIEICE Transactions on Information and Systems
    Issue number4
    Publication statusPublished - Apr 2011

    All Science Journal Classification (ASJC) codes

    • Software
    • Hardware and Architecture
    • Computer Vision and Pattern Recognition
    • Electrical and Electronic Engineering
    • Artificial Intelligence


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