A variable step-size adaptive algorithm under maximum correntropy criterion

Ren Wang; Badong Chen; Nanning Zheng; Jose C. Principe

doi:10.1109/IJCNN.2015.7280711

A variable step-size adaptive algorithm under maximum correntropy criterion

Ren Wang
, Badong Chen
, Nanning Zheng
, Jose C. Principe

Xi'an Jiaotong University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

15 Scopus citations

Abstract

Correntropy, a novel localized similarity measure defined in kernel space, has been successfully used as a cost function in adaptive system training. The adaptive algorithms under the maximum correntropy criterion (MCC) have been shown to be robust to impulsive non-Gaussian noises. However, they may converge slowly especially at a region far from the optimal solution. In this paper, we propose a new MCC algorithm with a variable step-size (VSS) called the VSS-MCC algorithm, which may achieve a much faster convergence speed while maintaining similar steady-state performance. In the new algorithm, the step-size is updated based on an approximation for the curvature of performance surface. Simulation results demonstrate the superior performance of VSS-MCC compared with the original MCC algorithm.

Original language	English
Title of host publication	2015 International Joint Conference on Neural Networks, IJCNN 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781479919604, 9781479919604, 9781479919604, 9781479919604
DOIs	https://doi.org/10.1109/IJCNN.2015.7280711
State	Published - 28 Sep 2015
Event	International Joint Conference on Neural Networks, IJCNN 2015 - Killarney, Ireland Duration: 12 Jul 2015 → 17 Jul 2015

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks
Volume	2015-September

Conference

Conference	International Joint Conference on Neural Networks, IJCNN 2015
Country/Territory	Ireland
City	Killarney
Period	12/07/15 → 17/07/15

Keywords

correntropy
maximum correntropy criterion (MCC) curvature
variable step size

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10.1109/IJCNN.2015.7280711

Cite this

Wang, R., Chen, B., Zheng, N., & Principe, J. C. (2015). A variable step-size adaptive algorithm under maximum correntropy criterion. In 2015 International Joint Conference on Neural Networks, IJCNN 2015 Article 7280711 (Proceedings of the International Joint Conference on Neural Networks; Vol. 2015-September). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2015.7280711

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title = "A variable step-size adaptive algorithm under maximum correntropy criterion",

abstract = "Correntropy, a novel localized similarity measure defined in kernel space, has been successfully used as a cost function in adaptive system training. The adaptive algorithms under the maximum correntropy criterion (MCC) have been shown to be robust to impulsive non-Gaussian noises. However, they may converge slowly especially at a region far from the optimal solution. In this paper, we propose a new MCC algorithm with a variable step-size (VSS) called the VSS-MCC algorithm, which may achieve a much faster convergence speed while maintaining similar steady-state performance. In the new algorithm, the step-size is updated based on an approximation for the curvature of performance surface. Simulation results demonstrate the superior performance of VSS-MCC compared with the original MCC algorithm.",

keywords = "correntropy, maximum correntropy criterion (MCC) curvature, variable step size",

author = "Ren Wang and Badong Chen and Nanning Zheng and Principe, \{Jose C.\}",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; International Joint Conference on Neural Networks, IJCNN 2015 ; Conference date: 12-07-2015 Through 17-07-2015",

year = "2015",

month = sep,

day = "28",

doi = "10.1109/IJCNN.2015.7280711",

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publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Wang, R, Chen, B , Zheng, N & Principe, JC 2015, A variable step-size adaptive algorithm under maximum correntropy criterion. in 2015 International Joint Conference on Neural Networks, IJCNN 2015., 7280711, Proceedings of the International Joint Conference on Neural Networks, vol. 2015-September, Institute of Electrical and Electronics Engineers Inc., International Joint Conference on Neural Networks, IJCNN 2015, Killarney, Ireland, 12/07/15. https://doi.org/10.1109/IJCNN.2015.7280711

A variable step-size adaptive algorithm under maximum correntropy criterion. / Wang, Ren; Chen, Badong ; Zheng, Nanning et al.
2015 International Joint Conference on Neural Networks, IJCNN 2015. Institute of Electrical and Electronics Engineers Inc., 2015. 7280711 (Proceedings of the International Joint Conference on Neural Networks; Vol. 2015-September).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A variable step-size adaptive algorithm under maximum correntropy criterion

AU - Wang, Ren

AU - Chen, Badong

AU - Zheng, Nanning

AU - Principe, Jose C.

PY - 2015/9/28

Y1 - 2015/9/28

N2 - Correntropy, a novel localized similarity measure defined in kernel space, has been successfully used as a cost function in adaptive system training. The adaptive algorithms under the maximum correntropy criterion (MCC) have been shown to be robust to impulsive non-Gaussian noises. However, they may converge slowly especially at a region far from the optimal solution. In this paper, we propose a new MCC algorithm with a variable step-size (VSS) called the VSS-MCC algorithm, which may achieve a much faster convergence speed while maintaining similar steady-state performance. In the new algorithm, the step-size is updated based on an approximation for the curvature of performance surface. Simulation results demonstrate the superior performance of VSS-MCC compared with the original MCC algorithm.

AB - Correntropy, a novel localized similarity measure defined in kernel space, has been successfully used as a cost function in adaptive system training. The adaptive algorithms under the maximum correntropy criterion (MCC) have been shown to be robust to impulsive non-Gaussian noises. However, they may converge slowly especially at a region far from the optimal solution. In this paper, we propose a new MCC algorithm with a variable step-size (VSS) called the VSS-MCC algorithm, which may achieve a much faster convergence speed while maintaining similar steady-state performance. In the new algorithm, the step-size is updated based on an approximation for the curvature of performance surface. Simulation results demonstrate the superior performance of VSS-MCC compared with the original MCC algorithm.

KW - correntropy

KW - maximum correntropy criterion (MCC) curvature

KW - variable step size

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U2 - 10.1109/IJCNN.2015.7280711

DO - 10.1109/IJCNN.2015.7280711

M3 - 会议稿件

AN - SCOPUS:84951016371

T3 - Proceedings of the International Joint Conference on Neural Networks

BT - 2015 International Joint Conference on Neural Networks, IJCNN 2015

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - International Joint Conference on Neural Networks, IJCNN 2015

Y2 - 12 July 2015 through 17 July 2015

ER -

A variable step-size adaptive algorithm under maximum correntropy criterion

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