An experimental comparison of dimensionality reduction for face verification methods

David Masip, Jordi Vitrià

    Research output: Contribution to journalReview articleResearchpeer-review

    2 Citations (Scopus)

    Abstract

    Two different approaches to dimensionality reduction techniques are analysed and evaluated, Locally Linear Embedding and a modification of Nonparametric Discriminant Analysis. Both are considered in order to be used in a face verification problem, as a previous step to nearest neighbor classification. LLE is focused in reducing the dimensionality of the space finding the nonlinear manifold underlying the data, while the goal of NDA is to find the most discriminative linear features of the input data that improve the classification rate (without making any prior assumption on the distribution). © Springer-Verlag Berlin Heidelberg 2003.
    Original languageEnglish
    Pages (from-to)530-537
    JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume2652
    Publication statusPublished - 1 Dec 2003

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