NDLI: Adaptive distributed sparsity-aware matrix decomposition

Content Provider	IEEE Xplore Digital Library
Author	Schizas, I.D.
Copyright Year	2013
Description	Author affiliation: Dept. of EE, Univ. of Texas at Arlington, Arlington, TX, USA (Schizas, I.D.)
Abstract	Data covariance matrices that consist of sparse factors arise in settings where the field sensed by a network of sensors is formed by localized sources. It is established that the task of identifying source-informative sensors boils down to estimating the support of the underlying sparse covariance factors. Relying on norm-one regularization a distributed sparsity-aware framework is developed. The associated minimization problems are solved using computationally efficient coordinate descent iterations that are combined with matrix deflation mechanisms. A simple scheme is also developed to set appropriately the sparsity-adjusting coefficients which can provably recover the support of a covariance matrix factor. Adaptive implementations that account for time-varying settings are also considered. The novel utilization of covariance sparsity does not require knowledge of the data model parameters, while numerical tests demonstrate that the novel schemes outperform existing alternatives.
Starting Page	4509
Ending Page	4513
File Size	230426
Page Count	5
File Format	PDF
ISBN	9781479903566
ISSN	15206149
DOI	10.1109/ICASSP.2013.6638513
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-05-26
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sensors Covariance matrices Matrix decomposition Sparse matrices Estimation Polynomials Minimization adaptive algorithms Distributed processing sparsity
Content Type	Text
Resource Type	Article

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