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• W2090582423 abstract "Conventional dictionary learning frameworks attempt to find a set of atoms that promote both signal representation and signal sparsity fora class of signals. In distributed compressive sensing (DCS), in addition to intra-signal correlation, inter-signal correlation is also exploited in the joint signal reconstruction, which goes beyond the aim of the conventional dictionary learning framework. In this letter, we propose a new dictionary learning framework in order to improve signal reconstruction performance in DCS applications. By capitalizing on the sparse common component and innovations (SCCI) model <citerefgrp xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink> <citeref refid=ref1/></citerefgrp> , which captures both intra- and inter-signal correlation, the proposed method iteratively finds a dictionary design that promotes various goals: i) signal representation; ii) intra-signal correlation; and iii) inter-signal correlation. Simulation results showthat our dictionary design leads to an improved DCS reconstruction performance in comparison to other designs." @default.
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• W2090582423 date "2015-01-01" @default.
• W2090582423 modified "2023-10-16" @default.
• W2090582423 title "Dictionary Design for Distributed Compressive Sensing" @default.
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• W2090582423 doi "https://doi.org/10.1109/lsp.2014.2350024" @default.
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