no code implementations • 10 May 2016 • Bijit Kumar Das, Mrityunjoy Chakraborty
Recently, the l0-least mean square (l0-LMS) algorithm has been proposed to identify sparse linear systems by employing a sparsity-promoting continuous function as an approximation of l0 pseudonorm penalty.
no code implementations • 10 May 2016 • Bijit Kumar Das, Mrityunjoy Chakraborty
The sparsity-aware zero attractor least mean square (ZA-LMS) algorithm manifests much lower misadjustment in strongly sparse environment than its sparsity-agnostic counterpart, the least mean square (LMS), but is shown to perform worse than the LMS when sparsity of the impulse response decreases.
no code implementations • 26 Oct 2014 • Bijit Kumar Das, Mrityunjoy Chakraborty, Jerónimo Arenas-García
In-network distributed estimation of sparse parameter vectors via diffusion LMS strategies has been studied and investigated in recent years.
