Local dimensionality reduction

S. Schaal, S. Vijayakumar, C.G. Atkeson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

If globally high dimensional data has locally only low dimensional distributions, it is advantageous to perform a local dimensionality reduction before further processing the data. In this paper we examine several techniques for local dimensionality reduction in the context of locally weighted linear regression. As possible candidates, we derive local versions of factor analysis regression, principle component regression, principle component regression on joint distributions, and partial least squares regression. After outlining the statistical bases of these methods, we perform Monte Carlo simulations to evaluate their robustness with respect to violations of their statistical assumptions. One surprising outcome is that locally weighted partial least squares regression offers the best average results, thus outperforming even factor analysis, the theoretically most appealing of our candidate techniques.
Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 10 (NIPS 1997)
PublisherNIPS Foundation
Pages633-639
Number of pages7
Publication statusPublished - 1998

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