Nonparametric discriminant analysis and nearest neighbor classification

M. Bressan, J. Vitrià

    Research output: Contribution to journalArticleResearchpeer-review

    94 Citations (Scopus)

    Abstract

    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
    Volume24
    Issue number15
    DOIs
    Publication statusPublished - 1 Jan 2003

    Keywords

    • Face recognition
    • Nearest neighbors classifier
    • Nonparametric discriminant analysis

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