Abstract
This paper studies a non-convexly constrained, sparse inverse problem in time-varying environments from a set theoretic estimation perspective. A new theory is developed that allows for the incorporation, in a unifying way, of different thresholding rules to promote sparsity, that may be even related to non-convex penalty functions. The resulted generalized thresholding operator is embodied in an efficient online, sparsity-aware learning scheme. The algorithm is of low computational complexity exhibiting a linear dependence on the number of free parameters. A convergence analysis of the proposed algorithm is conducted, and extensive experiments are also exhibited in order to validate the novel methodology.
Original language | English |
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Pages | 3277 - 3280 |
DOIs | |
Publication status | Published - 2012 |
Event | 37th IEEE International Conference on Acoustics, Speech and Signal Processing 2012 - Kyoto, Japan Duration: 25 Mar 2012 → 30 Mar 2012 |
Conference
Conference | 37th IEEE International Conference on Acoustics, Speech and Signal Processing 2012 |
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Abbreviated title | ICASSP 2012 |
Country/Territory | Japan |
City | Kyoto |
Period | 25/03/12 → 30/03/12 |