Name
Abstract | ||
|---|---|---|
The l1-norm sparsity constraint is a widely used technique for constructing sparse models. In this paper, two zeroattracting recursive least squares algorithms, which are referred to as ZA-RLS-I and ZA-RLS-II, are derived by employing the l1-norm of the parameter vector constraint to facilitate model sparsity. To achieve a closed-form solution, the l1-norm of the parameter vector is approximated b... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TVT.2016.2533664 | IEEE Transactions on Vehicular Technology |
Keywords | Field | DocType |
Algorithm design and analysis,Cost function,Approximation algorithms,Adaptation models,Channel estimation,Matching pursuit algorithms,Computational modeling | Least squares,Linear algebra,Approximation algorithm,Weighting,Algorithm design,Algorithm,Adaptive learning,Mathematics,Recursive least squares filter,Channel state information | Journal |
Volume | Issue | ISSN |
66 | 1 | 0018-9545 |
Citations | PageRank | References |
6 | 0.49 | 11 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| X Hong | 1 | 216 | 19.36 |
| Junbin Gao | 2 | 1112 | 119.67 |
| Sheng Chen | 3 | 1035 | 111.98 |