TY - GEN
T1 - Iteratively reweighted optimum linear regression in the presence of generalized Gaussian noise
AU - Wen, Fuxi
AU - Liu, Wei
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/3
Y1 - 2017/3
N2 - Generalized Gaussian distribution (GGD) is one of the most prominent and widely used parametric distributions to model the statistical properties of various phenomena. In this paper, we consider the linear regression problem in the presence of GGD noise employing the iteratively reweighted least squares (IRLS) algorithm. For the standard IRLS algorithm, an ℓp-norm minimization problem is solved iteratively. However, its performance depends on a properly chosen norm parameter p. To solve this problem, we propose a modified IRLS algorithm with a variable p, which is noise distribution dependent and can be updated online. Numerical studies show that the proposed method can normally converge within a few iterations. Furthermore, optimal performance is achieved in terms of normalized mean square error for different GGD noise models.
AB - Generalized Gaussian distribution (GGD) is one of the most prominent and widely used parametric distributions to model the statistical properties of various phenomena. In this paper, we consider the linear regression problem in the presence of GGD noise employing the iteratively reweighted least squares (IRLS) algorithm. For the standard IRLS algorithm, an ℓp-norm minimization problem is solved iteratively. However, its performance depends on a properly chosen norm parameter p. To solve this problem, we propose a modified IRLS algorithm with a variable p, which is noise distribution dependent and can be updated online. Numerical studies show that the proposed method can normally converge within a few iterations. Furthermore, optimal performance is achieved in terms of normalized mean square error for different GGD noise models.
KW - generalized Gaussian distribution
KW - Iteratively reweighted least squares
KW - linear regression
KW - shape parameter
KW - smoothed approximation
KW - ℓ-norm
UR - http://www.scopus.com/inward/record.url?scp=85016220157&partnerID=8YFLogxK
U2 - 10.1109/ICDSP.2016.7868640
DO - 10.1109/ICDSP.2016.7868640
M3 - Conference article published in proceeding or book
AN - SCOPUS:85016220157
T3 - International Conference on Digital Signal Processing, DSP
SP - 657
EP - 661
BT - Proceedings - 2016 IEEE International Conference on Digital Signal Processing, DSP 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE International Conference on Digital Signal Processing, DSP 2016
Y2 - 16 October 2016 through 18 October 2016
ER -