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Online Learning with Kernels a New Approach for Sparsity Control Based on a Coherence Criterion

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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.

Dates and versions

hal-02356365 , version 1 (08-11-2019)

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Jean-Baptiste Pothin, Cédric Richard. Online Learning with Kernels a New Approach for Sparsity Control Based on a Coherence Criterion. 2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing, Sep 2006, Arlington, United States. pp.241-245, ⟨10.1109/MLSP.2006.275555⟩. ⟨hal-02356365⟩
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