NDLI: Subspace penalized sparse learning for joint sparse recovery

Content Provider	IEEE Xplore Digital Library
Author	Jong Chul Ye Jong Min Kim Bresler, Y.
Copyright Year	2013
Description	Author affiliation: Coordinated Sci. Lab., Univ. of Illinois at Urbana-Champaign., Urbana, IL, USA (Bresler, Y.) \|\| Dept. of Bio/Brain Eng., KAIST, Daejeon, South Korea (Jong Chul Ye; Jong Min Kim)
Abstract	The multiple measurement vector problem (MMV) is a generalization of the compressed sensing problem that addresses the recovery of a set of jointly sparse signal vectors. One of the important contributions of this paper is to reveal that the seemingly least related state-of-art MMV joint sparse recovery algorithms - M-SBL (multiple sparse Bayesian learning) and subspace-based hybrid greedy algorithms - have a very important link. More specifically, we show that replacing the log det(·) term in M-SBL by a log det(·) rank proxy that exploits the spark reduction property discovered in subspace-based joint sparse recovery algorithms, provides significant improvements. Theoretical analysis demonstrates that even thoughM-SBL is often unable to remove all localminimizers, the proposed method can do so under fairly mild conditions, without affecting the global minimizer.
Starting Page	6039
Ending Page	6042
File Size	195766
Page Count	4
File Format	PDF
ISBN	9781479903566
ISSN	15206149
DOI	10.1109/ICASSP.2013.6638824
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	Joints Multiple signal classification Bayes methods Algorithm design and analysis Sensors Signal to noise ratio Signal processing algorithms multiple measurement vector problem Compresse sensing joint sparse recovery
Content Type	Text
Resource Type	Article

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