Adaptive Metric Kernel Regression

Cyril Goutte, Jan Larsen

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    Abstract

    Kernel smoothing is a widely used nonparametric pattern recognition technique. By nature, it suffers from the curse of dimensionality and is usually difficult to apply to high input dimensions. In this paper, we propose an algorithm that adapts the input metric used in multivariate regression by minimising a cross-validation estimate of the generalisation error. This allows one to automatically adjust the importance of different dimensions. The improvement in terms of modelling performance is illustrated on a variable selection task where the adaptive metric kernel clearly outperforms the standard approach
    Original languageEnglish
    Title of host publicationNeural Networks for Signal Processing VIII, 1998. Proceedings of the 1998 IEEE Signal Processing Society Workshop
    Place of PublicationPiscataway
    PublisherIEEE
    Publication date1998
    Pages184-193
    ISBN (Print)0-7803-5060-X
    DOIs
    Publication statusPublished - 1998
    EventNNSP´98, Neural Networks for Signal Processing VIII - Cambridge, U.K.
    Duration: 1 Jan 1998 → …

    Conference

    ConferenceNNSP´98, Neural Networks for Signal Processing VIII
    CityCambridge, U.K.
    Period01/01/1998 → …

    Bibliographical note

    Copyright: 1998 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE

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