Sparse kernel orthonormalized PLS for feature extraction in large datasets

Jerónimo Arenas-García, Kaare Brandt Petersen, Lars Kai Hansen

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    Abstract

    In this paper we are presenting a novel multivariate analysis method for large scale problems. Our scheme is based on a novel kernel orthonormalized partial least squares (PLS) variant for feature extraction, imposing sparsity constrains in the solution to improve scalability. The algorithm is tested on a benchmark of UCI data sets, and on the analysis of integrated short-time music features for genre prediction. The upshot is that the method has strong expressive power even with rather few features, is clearly outperforming the ordinary kernel PLS, and therefore is an appealing method for feature extraction of labelled data.
    Original languageEnglish
    Title of host publicationNIPS 2006
    Publication date2006
    Publication statusPublished - 2006
    EventNIPS 2006 -
    Duration: 1 Jan 2006 → …

    Conference

    ConferenceNIPS 2006
    Period01/01/2006 → …

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