Variable regularized least-squares algorithm: One-step-ahead cost function with equivalent optimality

Authors:
Moonsoo Chang;Namwoong Kong;Poogyeon Park
Affiliations:
Department of Electronic and Electrical Engineering, Pohang University of Science and Technology, Pohang, Republic of Korea;Department of Electronic and Electrical Engineering, Pohang University of Science and Technology, Pohang, Republic of Korea;Department of Electronic and Electrical Engineering, Pohang University of Science and Technology, Pohang, Republic of Korea and Division of Information Technology of Convergence Engineering, Pohan ...
Venue:
Signal Processing
Year:
2011

Citing 3
Cited 3

Displacement structure: theory and applications

SIAM Review
A noise resilient variable step-size LMS algorithm

Signal Processing
A family of adaptive filter algorithms with decorrelatingproperties

IEEE Transactions on Signal Processing

A two-stage affine projection algorithm with mean-square-error-matching step-sizes

Signal Processing
Variable matrix-type step-size affine projection algorithm with orthogonalized input vectors

Signal Processing
Fast communication: Adaptive combination of affine projection and NLMS algorithms

Signal Processing

Quantified Score

Hi-index	0.08

Visualization

Abstract

This paper proposes a new variable regularized least-squares (VR-LS) algorithm by recursively constructing a weighting scalar of the regularized least-squares (LS) cost function. Since the recursive LS (RLS) algorithm provides the best performances by all of VR-LS algorithms, the design objective of the weighting scalar is chosen such that equivalent optimality is ensured between one-step-ahead cost functions of the RLS and of the VR-LS algorithm. The proposed VR-LS algorithm functions similarly as the RLS with uncorrelated inputs; however, this is not the case with colored (correlated) inputs. Therefore, a conventional filtering technique is applied to both on the inputs and on the desired signals so as to obtain whitened inputs. This enables the proposed algorithm handle the case of correlated inputs.