Nonparametric discriminant analysis and nearest neighbor classification

M. Bressan, J. Vitrià

    Research output: Contribution to journalArticleResearchpeer-review

    96 Citations (Scopus)


    Nonparametric discriminant analysis (NDA), opposite to other nonparametric techniques, has received little or no attention within the pattern recognition community. Nearest neighbor classification (NN) instead, has a well established position among other classification techniques due to its practical and theoretical properties. In this paper, we observe that when we seek a linear representation adapted to improve NN performance, what we obtain not surprisingly is quite close to NDA. Since a hierarchy is provided on the extracted features it also serves as a dimensionality reduction technique that preserves NN performance. Experiments evaluate and compare NN classification using our proposed representation against more classical feature extraction techniques. © 2003 Elsevier B.V. All rights reserved.
    Original languageEnglish
    Pages (from-to)2743-2749
    JournalPattern Recognition Letters
    Issue number15
    Publication statusPublished - 1 Jan 2003


    • Face recognition
    • Nearest neighbors classifier
    • Nonparametric discriminant analysis


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