Adaptive regularized canonical correlations in clustering sensor data

Jia Chen, Ioannis D. Schizas

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

Abstract

A regularized canonical correlations scheme is proposed for adaptive clustering of sensor measurements according to their information content. A novel framework utilizing sparsity-inducing regularization and exponential weighing is designed to deal with nonstationary settings. Distributed recursions to minimize the proposed formulation are put forth by utilizing coordinate descent techniques combined with the alternating direction method of multipliers. Numerical tests demonstrate that the novel adaptive clustering framework is capable to deal with nonstationary settings while outperforming existing alternatives.

Original languageEnglish (US)
Title of host publicationConference Record of the 48th Asilomar Conference on Signals, Systems and Computers
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages1611-1615
Number of pages5
ISBN (Electronic)9781479982974
DOIs
StatePublished - Apr 24 2015
Event48th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 - Pacific Grove, United States
Duration: Nov 2 2014Nov 5 2014

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2015-April
ISSN (Print)1058-6393

Other

Other48th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015
CountryUnited States
CityPacific Grove
Period11/2/1411/5/14

Keywords

  • Adaptive
  • canonical correlation analysis
  • non-stationary data
  • sparsity

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