Exploiting covariance-domain sparsity for dimensionality reduction

Ioannis D. Schizas, Georgios B Giannakis, Nikolaos Sidiropoulos

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

4 Scopus citations

Abstract

Novel schemes are developed for linear dimensionality reduction of data vectors whose covariance matrix exhibits sparsity. Two types of sparsity are considered: i) sparsity in the eigenspace of the covariance matrix; or, ii) sparsity in the factors that the covariance matrix is decomposed. Different from existing alternatives, the novel dimensionality-reducing and reconstruction matrices are designed to fully exploit covariance-domain sparsity. They are obtained by solving properly formulated optimization problems using simple coordinate descent iterations. Numerical tests corroborate that the novel algorithms achieve improved reconstruction quality relative to related approaches that do not fully exploit covariance-domain sparsity.

Original languageEnglish (US)
Title of host publicationCAMSAP 2009 - 2009 3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing
Pages117-120
Number of pages4
DOIs
StatePublished - Dec 1 2009
Event2009 3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2009 - Aruba, Netherlands
Duration: Dec 13 2009Dec 16 2009

Publication series

NameCAMSAP 2009 - 2009 3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing

Other

Other2009 3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2009
CountryNetherlands
CityAruba
Period12/13/0912/16/09

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