Regularized canonical correlations for sensor data clustering

Jia Chen, Ioannis D. Schizas

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

Abstract

The task of determining informative sensors and clustering the sensor measurements according to their information content is considered. To this end, the standard canonical correlation analysis (CCA) framework is equipped with norm-one and norm-two regularization terms to estimate the unknown number of field sources and identify informative groups of sensors. Coordinate descent techniques are combined with the alternating direction method of multipliers to derive an algorithm that minimizes the regularized CCA framework. An efficient scheme to properly select the regularization coefficients associated with the norm-one and norm-two terms is also developed. Numerical tests corroborate that the novel scheme outperforms existing alternatives.

Original languageEnglish (US)
Title of host publication2015 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3601-3605
Number of pages5
ISBN (Electronic)9781467369978
DOIs
StatePublished - Aug 4 2015
Event40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Brisbane, Australia
Duration: Apr 19 2014Apr 24 2014

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2015-August
ISSN (Print)1520-6149

Other

Other40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015
CountryAustralia
CityBrisbane
Period4/19/144/24/14

Keywords

  • Canonical correlation analysis
  • clustering
  • optimization
  • sparsity

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