Orthogonal least squares regression with tunable kernels

S. Chen; X. X. Wang; D. J. Brown

doi:10.1049/el:20050265

Orthogonal least squares regression with tunable kernels

S. Chen^*
, X. X. Wang
, D. J. Brown

^*Corresponding author for this work

Portsmouth University

Research output: Contribution to journal › Article › peer-review

14 Citations (Scopus)

16 Downloads (Pure)

Abstract

A novel technique is proposed to construct sparse regression models based on the orthogonal least squares method with tunable kernels. The proposed technique tunes the centre vector and diagonal covariance matrix of individual regressors by incrementally minimising the training mean square error using a guided random search algorithm, and it offers a state-of-the-art method for constructing very sparse models that generalise well.

Original language	English
Pages (from-to)	484-486
Number of pages	3
Journal	Electronics letters
Volume	41
Issue number	8
DOIs	https://doi.org/10.1049/el:20050265
Publication status	Published - 14 Apr 2005

Bibliographical note

© 2005 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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10.1049/el:20050265

Orthogonal Least Squares Regression with Tunable Kernels
© 2005 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Cite this

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Orthogonal least squares regression with tunable kernels

Abstract

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