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• W1985298333 abstract "This paper introduces the usage of non-convex based regularizers to solve the underdetermined MEG inverse problem. The signal to be reconstructed is considered to have a structure which entails group-wise sparsity and within group sparsity among its covariates. We discuss the usage of l 2 norm regularization and smoothed l 0 (SL0) norm regularization to impose group-wise and within group sparsity respectively. In addition, we introduce a novel criterion which if satisfied, guarantees global optimality while solving this non-convex optimization problem. We use proximal gradient descent as the method of optimization as it promises faster convergence rates. We show that our algorithm can successfully recover a sparse signal with a smaller number of measurements than the conventional l 1 regularization framework." @default.
• W1985298333 created "2016-06-24" @default.
• W1985298333 creator A5056915498 @default.
• W1985298333 creator A5071562081 @default.
• W1985298333 date "2014-12-01" @default.
• W1985298333 modified "2023-09-25" @default.
• W1985298333 title "Grouped sparse signal reconstruction using non-convex regularizers" @default.
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• W1985298333 doi "https://doi.org/10.1109/globalsip.2014.7032168" @default.
• W1985298333 hasPublicationYear "2014" @default.
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