Subset Selection for Kernel-Based Signal Reconstruction

Mario Coutino, Sundeep Prabhakar Chepuri, Geert Leus

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages4014-4018
Number of pages5
ISBN (Electronic)978-1-5386-4658-8
ISBN (Print)978-1-5386-4659-5
DOIs
Publication statusPublished - 2018
Event2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018: Signal Processing and Artificial Intelligence: Changing the World - Calgary Telus Convention Center (CTCC), Calgary, Canada
Duration: 15 Apr 201820 Apr 2018
https://2018.ieeeicassp.org

Conference

Conference2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
CountryCanada
CityCalgary
Period15/04/1820/04/18
Internet address

Keywords

  • Kernel regression
  • Kernel-based signal reconstruction
  • Optimal subset selection
  • Sensor selection
  • Sub-modularity

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  • Cite this

    Coutino, M., Chepuri, S. P., & Leus, G. (2018). Subset Selection for Kernel-Based Signal Reconstruction. In 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings (pp. 4014-4018). [8461510] IEEE. https://doi.org/10.1109/ICASSP.2018.8461510