Sparse LMS for system identification

By Yilun Chen, Yuantao Gu, A. Hero. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2009

754 citations recorded by the source corpus.

Subfields: Computational Mechanics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/24b561de4de901a311adf9180f9f006409b4f3d9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. Ali H. Sayed
2. Georgios B. Giannakis
3. Juan Andrés Bazerque
4. Daniele Angelosante
5. Wei Shi
6. Wotao Yin
7. Angelia Nedić
8. Kun Yuan

In Signal Processing

1. Ali H. Sayed
2. Georgios B. Giannakis
3. Juan Andrés Bazerque
4. Daniele Angelosante
5. Lieven De Lathauwer
6. Jianshu Chen
7. Huseyin Arslan
8. Tevfik Yücek

Citations in the corpus

• Adaptive Networks (2014, background): “The choice (122) has the advantage of selectively shrinking those components of the iterate w k ; i (cid:5) 1 whose magnitudes are comparable to (cid:14) with little effect on components whose magnitudes are much larger than [162], [164], [165].”
• Online Adaptive Estimation of Sparse Signals: Where RLS Meets the $\ell_1$ -Norm (2010, methodology): “Sequential noise-free signal recovery w as considered in [18], and a sparsity-aware least mean-square (LMS) algorithm was pursued in [14].”