Orthogonal least squares regression with tunable kernels

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

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review


    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 languageEnglish
    Pages (from-to)484-486
    Number of pages3
    JournalElectronics letters
    Issue number8
    Publication statusPublished - 14 Apr 2005

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