The Experts below are selected from a list of 1305 Experts worldwide ranked by ideXlab platform
Bhaskar D Rao - One of the best experts on this subject based on the ideXlab platform.
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sparse signal recovery with temporally correlated source vectors using sparse bayesian learning
IEEE Journal of Selected Topics in Signal Processing, 2011Co-Authors: Zhilin Zhang, Bhaskar D RaoAbstract:We address the sparse signal recovery problem in the context of multiple measurement vectors (MMV) when elements in each Nonzero Row of the solution matrix are temporally correlated. Existing algorithms do not consider such temporal correlation and thus their performance degrades significantly with the correlation. In this paper, we propose a block sparse Bayesian learning framework which models the temporal correlation. We derive two sparse Bayesian learning (SBL) algorithms, which have superior recovery performance compared to existing algorithms, especially in the presence of high temporal correlation. Furthermore, our algorithms are better at handling highly underdetermined problems and require less Row-sparsity on the solution matrix. We also provide analysis of the global and local minima of their cost function, and show that the SBL cost function has the very desirable property that the global minimum is at the sparsest solution to the MMV problem. Extensive experiments also provide some interesting results that motivate future theoretical research on the MMV model.
Zhilin Zhang - One of the best experts on this subject based on the ideXlab platform.
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sparse signal recovery with temporally correlated source vectors using sparse bayesian learning
IEEE Journal of Selected Topics in Signal Processing, 2011Co-Authors: Zhilin Zhang, Bhaskar D RaoAbstract:We address the sparse signal recovery problem in the context of multiple measurement vectors (MMV) when elements in each Nonzero Row of the solution matrix are temporally correlated. Existing algorithms do not consider such temporal correlation and thus their performance degrades significantly with the correlation. In this paper, we propose a block sparse Bayesian learning framework which models the temporal correlation. We derive two sparse Bayesian learning (SBL) algorithms, which have superior recovery performance compared to existing algorithms, especially in the presence of high temporal correlation. Furthermore, our algorithms are better at handling highly underdetermined problems and require less Row-sparsity on the solution matrix. We also provide analysis of the global and local minima of their cost function, and show that the SBL cost function has the very desirable property that the global minimum is at the sparsest solution to the MMV problem. Extensive experiments also provide some interesting results that motivate future theoretical research on the MMV model.
Rao, Bhaskar D. - One of the best experts on this subject based on the ideXlab platform.
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Sparse Signal Recovery with Temporally Correlated Source Vectors Using Sparse Bayesian Learning
'Institute of Electrical and Electronics Engineers (IEEE)', 2011Co-Authors: Zhang Zhilin, Rao, Bhaskar D.Abstract:We address the sparse signal recovery problem in the context of multiple measurement vectors (MMV) when elements in each Nonzero Row of the solution matrix are temporally correlated. Existing algorithms do not consider such temporal correlations and thus their performance degrades significantly with the correlations. In this work, we propose a block sparse Bayesian learning framework which models the temporal correlations. In this framework we derive two sparse Bayesian learning (SBL) algorithms, which have superior recovery performance compared to existing algorithms, especially in the presence of high temporal correlations. Furthermore, our algorithms are better at handling highly underdetermined problems and require less Row-sparsity on the solution matrix. We also provide analysis of the global and local minima of their cost function, and show that the SBL cost function has the very desirable property that the global minimum is at the sparsest solution to the MMV problem. Extensive experiments also provide some interesting results that motivate future theoretical research on the MMV model.Comment: The final version with some typos corrected. Codes can be downloaded at: http://dsp.ucsd.edu/~zhilin/TSBL_code.zi
Scott Kelsey - One of the best experts on this subject based on the ideXlab platform.
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Congruences for Generalized Frobenius Partitions of Nonzero Row Difference
2019Co-Authors: Scott KelseyAbstract:We extend George Andrew's general principle for counting generalized Frobenius partitions to include arrays with Nonzero Row difference and establish some congruences for these arrays.Comment: 5 page
Kelsey Scott - One of the best experts on this subject based on the ideXlab platform.
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congruences for generalized frobenius partitions of Nonzero Row difference
arXiv: Combinatorics, 2019Co-Authors: Kelsey ScottAbstract:We extend George Andrew's general principle for counting generalized Frobenius partitions to include arrays with Nonzero Row difference and establish some congruences for these arrays.
