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Can I read Adaptive support-driven Bayesian reweighted algorithm for sparse signal recovery on EtoBox?
Adaptive support-driven Bayesian reweighted algorithm for sparse signal recovery by Junlin Li; Wei Zhou; Cheng Cheng is a Computer Science article available to read on EtoBox.
What is Adaptive support-driven Bayesian reweighted algorithm for sparse signal recovery about?
This paper presents an adaptive support-driven Bayesian reweighted algorithm based on shrinkage thresholding, for largescale sparse signal recovery. The proposed algorithm is based on the widely used sparse Bayesian learning (SBL) methods, which relies on parameterized prior. SBL leads to a nonconvex optimization problem that encourages model with few nonzero coefficients. In SBL, sparse signal recovery problems can be solved by a reweighted 1 -regularization algorithm. However, this algorithm is expensive in computation and memory, especially for large-scale problem. To alleviate this problem, we proposed a restart strategy leading to the adaptive support estimate, which reduces the computational burden and memory simultaneously. Numerical experiments demonstrate the effectiveness and computational advantages of the proposed algorithm.
Who reads Adaptive support-driven Bayesian reweighted algorithm for sparse signal recovery?
It is typically read by researchers, students, and practitioners in Computer Science.
- Author
- Junlin Li; Wei Zhou; Cheng Cheng
- Publisher
- Springer Science and Business Media LLC
- Published
- 2021
- Language
- EN
- Field
- Computer Science (Physical Sciences)