Paper Info
Title
Exploiting the convex-concave penalty for tracking: A novel dynamic reweighted sparse Bayesian learning algorithm
Abstract
We propose a novel dynamic reweighted ℓ2 (DRℓ2) algorithm in the regime of dynamic compressive sensing. Our analysis shows that aiming to solve a Type II optimization problem, DRℓ2 is effectively minimizing a `convex-concave' penalty in the coefficients that transitions from a convex region to a concave function using knowledge of past estimations. DRℓ2 thus provides superior reconstruction performance compared with state-of-the-art dynamic CS algorithms.
Year
DOI
Venue
2014
10.1109/ICASSP.2014.6854220
ICASSP
Keywords
Field
DocType
optimisation,past estimation knowledge,dynamic compressive sensing,type ii optimization problem,bayes methods,compressed sensing,dynamic reweighted sparse bayesian learning algorithm,signal reconstruction,convex-concave penalty minimisation,superior reconstruction performance,vectors,signal processing,estimation
Mathematical optimization,Bayesian inference,Pattern recognition,Computer science,Concave function,Algorithm,Regular polygon,Artificial intelligence,Optimization problem,Compressed sensing
Conference
ISSN
Citations 
PageRank 
1520-6149
2
0.36
References 
Authors
7
4
Name
Order
Citations
PageRank
Yu Wang1598.66
David P. Wipf258446.31
Wei Chen3266.38
Ian J. Wassell428835.10