Distributed sparse canonical correlation analysis in clustering sensor data

Jia Chen, Ioannis D. Schizas

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

5 Scopus citations

Abstract

The problem of determining information-bearing sensors in the presence of multiple field sources and (non-)linear data models is considered. To this end, a novel canonical correlation analysis (CCA) framework combined with norm-one regularization is introduced to identify correlated measurements across the distributed sensors and cluster the sensor data based on their source content. A distributed algorithm is also put forth for informative sensor identification in nonlinear settings using the novel CCA approach. Toward this end, the sparsity-aware CCA framework is reformulated as a separable constrained minimization problem which is solved by utilizing block coordinate descent techniques combined with the alternating direction method of multipliers. Numerical tests demonstrate that the distributed sparse CCA scheme put forth outperforms existing alternatives when it comes to clustering the sensor data based on their source content.

Original languageEnglish (US)
Title of host publicationConference Record of the 47th Asilomar Conference on Signals, Systems and Computers
PublisherIEEE Computer Society
Pages639-643
Number of pages5
ISBN (Print)9781479923908
DOIs
StatePublished - Jan 1 2013
Externally publishedYes
Event2013 47th Asilomar Conference on Signals, Systems and Computers - Pacific Grove, CA, United States
Duration: Nov 3 2013Nov 6 2013

Other

Other2013 47th Asilomar Conference on Signals, Systems and Computers
CountryUnited States
CityPacific Grove, CA
Period11/3/1311/6/13

Keywords

  • canonical correlation analysis
  • Distributed processing
  • sparsity

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  • Cite this

    Chen, J., & Schizas, I. D. (2013). Distributed sparse canonical correlation analysis in clustering sensor data. In Conference Record of the 47th Asilomar Conference on Signals, Systems and Computers (pp. 639-643). [6810359] IEEE Computer Society. https://doi.org/10.1109/ACSSC.2013.6810359