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.
|Pages||3277 - 3280|
|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||37th IEEE International Conference on Acoustics, Speech and Signal Processing 2012|
|Abbreviated title||ICASSP 2012|
|Period||25/03/12 → 30/03/12|