Optimized measurements for kernel compressive sensing

Karthikeyan Natesan Ramamurthy, Andreas Spanias

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Certain classes of signals can be well approximated using a few principal components in the feature space, that is obtained by a non-linear transformation of the input signal space. Compressive sensing of such signals with random measurements can be performed using the kernel trick. In this paper, we propose a procedure to compute optimized measurement vectors for kernel compressive sensing. We show that the optimized measurements correspond to the data samples that have the highest energy when projected onto the kernel principal components. Simulation results obtained with handwritten digits and the sculpted faces dataset show that the proposed measurement system results in a substantially better recovery when compared to using the same number of random measurements.

Original languageEnglish (US)
Title of host publicationConference Record of the 45th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2011
Pages1443-1446
Number of pages4
DOIs
StatePublished - Dec 1 2011
Event45th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2011 - Pacific Grove, CA, United States
Duration: Nov 6 2011Nov 9 2011

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393

Other

Other45th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2011
CountryUnited States
CityPacific Grove, CA
Period11/6/1111/9/11

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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