Paper Info
Title
Group Greedy RLS Sparsity Estimation via Information Theoretic Criteria
Abstract
This work introduces a group sparse adaptive greedy algorithm that uses information theoretic criteria (ITC) to estimate online the sparsity level. The algorithm selects a set of candidate groups using group neighbor permutations and maintains a partial QR decomposition to compute the solution. It contains a mechanism that allows group joining which, complementing the splitting of groups, produces a robust algorithm. We focus here on a study of the ITC use, namely the predictive least squares (PLS) and Bayesian information criterion (BIC), in conjunction with the group sparse algorithm. We propose several forms of group oriented ITC and evaluate them with extensive simulations for a time-varying channel identification problem. Compared to the non group aware counterparts, the performance is improved at the cost of higher complexity. The best results are given by a group PLS criterion directly generalizing the standard PLS.
Year
DOI
Venue
2013
10.1109/CSCS.2013.26
CSCS '13 Proceedings of the 2013 19th International Conference on Control Systems and Computer Science
Keywords
Field
DocType
standard pls,non group,candidate group,group neighbor permutation,itc use,greedy algorithm,robust algorithm,information theoretic criteria,group pls criterion,group sparse adaptive,group greedy rls sparsity,group sparse algorithm,recursive least squares,computational complexity,silicon,information theory,estimation,sparse matrices,model selection,bayesian information criterion
Information theory,Bayesian information criterion,Pattern recognition,Permutation,Model selection,Algorithm,Greedy algorithm,Artificial intelligence,Mathematics,QR decomposition,Recursive least squares filter,Computational complexity theory
Conference
ISSN
ISBN
Citations 
2379-0474
978-1-4673-6140-8
0
PageRank 
References 
Authors
0.34
8
2
Name
Order
Citations
PageRank
Alexandru Onose1123.93
Bogdan Dumitrescu210722.76