Subset Selection for Kernel-Based Signal Reconstruction

Conference Paper (2018)
Author(s)

M.A. Coutiño (TU Delft - Signal Processing Systems)

S. P. Chepuri (TU Delft - Signal Processing Systems)

G.J.T. Leus (TU Delft - Signal Processing Systems)

Research Group
Signal Processing Systems
Copyright
© 2018 Mario Coutino, S.P. Chepuri, G.J.T. Leus
DOI related publication
https://doi.org/10.1109/ICASSP.2018.8461510
More Info
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Publication Year
2018
Language
English
Copyright
© 2018 Mario Coutino, S.P. Chepuri, G.J.T. Leus
Research Group
Signal Processing Systems
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.@en
Pages (from-to)
4014-4018
ISBN (print)
978-1-5386-4659-5
ISBN (electronic)
978-1-5386-4658-8
Reuse Rights

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Abstract

In this work, we introduce subset selection strategies for signal reconstruction based on kernel methods, particularly for the case of kernel-ridge regression. Typically, these methods are employed for exploiting known prior information about the structure of the signal of interest. We use the mean squared error and a scalar function of the covariance matrix of the kernel regressors to establish metrics for the subset selection problem. Despite the NP-hard nature of the problem, we introduce efficient algorithms for finding approximate solutions for the proposed metrics. Finally, numerical experiments demonstrate the applicability of the proposed strategies.

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