Iterative Re-weighted Least Squares Algorithms for Non-negative Sparse and Group-sparse Recovery

被引：2
|
作者：
Majumdar, Angshul [1 ]
机构：
[1] IIIT Delhi, Delhi, India
来源：
2022 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) | 2022年
关键词：
non-negative sparse recovery; non-negative group sparsity; MINIMIZATION;
D O I：
10.1109/ICASSP43922.2022.9747175
中图分类号：
O42 [声学];
学科分类号：
070206 ; 082403 ;
摘要：
Algorithms for non-negative sparse recovery are either based on modifications of orthogonal matching pursuit or are based on thresholding of non-negative least squares. Both are variants of techniques proposed for sparse recovery. This work is based on the iterative re-weighted least squares (IRLS) approach for sparse recovery. IRLS has been found to be a simple yet versatile approach that can handle both l(1)-norm and l(p)-quasi norm (0<p<1). We extend this approach to handle not only sparse recovery but also group-sparse recovery.
引用
收藏
页码：4423 / 4427
页数：5
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