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10.1109/ACSSC.2014.7094813 http://scihub22266oqcxt.onion/10.1109/ACSSC.2014.7094813 C5667949!5667949!29104413     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=29104413&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Conf+Rec+Asilomar+Conf+Signals+Syst+Comput 2014 ; 2014 (ä): 1962-6 Nephropedia Template TP {{tp|p=29104413|t=2014. Hierarchical Bayesian Approach For Jointly-Sparse Solution Of Multiple-Measurement Vectors |pdf=|usr=}}{{29104413}} gab.com Text Hierarchical Bayesian Approach For Jointly-Sparse Solution Of Multiple-Measurement Vectors JO: Conf Rec Asilomar Conf Signals Syst Comput http://www.kidney.de/pget.php?&tw=1&pmid=29104413 #FOAvip It is well-known that many signals of interest can be well-estimated via just a small number of supports under some specific basis. Here, we consider finding sparse solution for Multiple Measurement Vectors (MMVs) in case of having both jointly sparse and clumpy structure. Most of the previous work for finding such sparse representations are based on greedy and sub-optimal algorithms such as Basis Pursuit (BP), Matching Pursuit (MP), and Orthogonal Matching Pursuit (OMP). In this paper, we first propose a hierarchical Bayesian model to deal with MMVs that have jointly-sparse structure in their solutions. Then, the model is modified to account for clumps of the neighbor supports (block sparsity) in the solution structure, as well. Several examples are considered to illustrate the merit of the proposed hierarchical Bayesian model compared to OMP and a modified version of the OMP algorithm. Twit Text FOAVip Hierarchical Bayesian Approach For Jointly-Sparse Solution Of Multiple-Measurement Vectors ????Conf Rec Asilomar Conf Signals Syst Comput http://www.kidney.de/pget.php?&tw=1&pmid=29104413 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=29104413 #FOAvip English Wikipedia <ref>{{Cite pmid|29104413}}</ref> Hierarchical Bayesian Approach For Jointly-Sparse Solution Of Multiple-Measurement Vectors #MMPMID29104413 Shekaramiz M; Moon TK; Gunther JH Conf Rec Asilomar Conf Signals Syst Comput 2014[Nov]; 2014 (ä): 1962-6 PMID29104413show ga It is well-known that many signals of interest can be well-estimated via just a small number of supports under some specific basis. Here, we consider finding sparse solution for Multiple Measurement Vectors (MMVs) in case of having both jointly sparse and clumpy structure. Most of the previous work for finding such sparse representations are based on greedy and sub-optimal algorithms such as Basis Pursuit (BP), Matching Pursuit (MP), and Orthogonal Matching Pursuit (OMP). In this paper, we first propose a hierarchical Bayesian model to deal with MMVs that have jointly-sparse structure in their solutions. Then, the model is modified to account for clumps of the neighbor supports (block sparsity) in the solution structure, as well. Several examples are considered to illustrate the merit of the proposed hierarchical Bayesian model compared to OMP and a modified version of the OMP algorithm. äHierarchical Bayesian Approach For Jointly-Sparse Solution Of Multiple(epmc).pdf DeepDyve Pubget Overpricing