ONLINE LEARNING WITH KERNELS A NEW APPROACH FOR SPARSITY CONTROL BASED ON A COHERENCE CRITERION

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ONLINE LEARNING WITH KERNELS A NEW APPROACH FOR SPARSITY CONTROL BASED ON A COHERENCE CRITERION

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by Jean-baptiste Pothin , Cédric Richard

BibTeX

@MISC{Pothin_onlinelearning,
    author = {Jean-baptiste Pothin and Cédric Richard},
    title = {ONLINE LEARNING WITH KERNELS A NEW APPROACH FOR SPARSITY CONTROL BASED ON A COHERENCE CRITERION},
    year = {}
}

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Abstract

Kernel methods are well known standard tools for solving function approximation and pattern classification problems. In this paper, we consider online learning in a reproducing kernel Hilbert space. We develop a simple and computationally efficient algorithm for sparse solutions. The approach is based on sequential projection learning and the coherence criterion, which is a fundamental parameter to characterize dictionaries of functions in sparse approximation problems. Experimental results show the effectiveness of our approach. 1.

Keyphrases

pattern classification problem function approximation sequential projection learning fundamental parameter sparse approximation problem efficient algorithm standard tool kernel method coherence criterion online learning reproducing kernel hilbert space sparse solution experimental result

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