Cart
Create Account
Sign In
Already purchased this program?
Login to View
This video program is a part of the Premium package:
Normalized Least-Mean-Square Algorithms With Minimax Concave Penalty
- IEEE MemberUS $11.00
- Society MemberUS $0.00
- IEEE Student MemberUS $11.00
- Non-IEEE MemberUS $15.00
Normalized Least-Mean-Square Algorithms With Minimax Concave Penalty
We propose a novel problem formulation for sparsity-aware adaptive filtering based on the nonconvex minimax concave (MC) penalty, aiming to obtain a sparse solution with small estimation bias. We present two algorithms: the first algorithm uses a single f
We propose a novel problem formulation for sparsity-aware adaptive filtering based on the nonconvex minimax concave (MC) penalty, aiming to obtain a sparse solution with small estimation bias. We present two algorithms: the first algorithm uses a single f
Next Up
00:28:33
