Adaptive Kernel-width Estimation for Learning Algorithms based on Maximum Zero-Error Probability

2021 ◽  
Vol 58 (10) ◽  
pp. 3-8
Author(s):  
Namyong Kim ◽  
Kihyeon Kwon
Keyword(s):  
Error Probability ◽  
Learning Algorithms ◽  
Kernel Width ◽  
Adaptive Kernel

Kernel online learning with adaptive kernel width

2016 ◽  
Vol 175 ◽  
pp. 233-242 ◽  
Author(s):  
Haijin Fan ◽  
Qing Song ◽  
Sumit B. Shrestha
Keyword(s):  
Online Learning ◽  
Kernel Width ◽  
Adaptive Kernel

Convergence performance analysis of an adaptive kernel width MCC algorithm

2017 ◽  
Vol 76 ◽  
pp. 71-76 ◽  
Author(s):  
Weihua Wang ◽  
Jihong Zhao ◽  
Hua Qu ◽  
Badong Chen ◽  
Jose C. Principe
Keyword(s):  
Performance Analysis ◽  
Kernel Width ◽  
Adaptive Kernel ◽  
Convergence Performance

scholarly journals Performance evaluation of the maximum complex correntropy criterion with adaptive kernel width update

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Manoel B. L. Aquino ◽  
João P. F. Guimarães ◽  
Leandro L. S. Linhares ◽  
Aluísio I. R. Fontes ◽  
Allan M. Martins
Keyword(s):  
Free Parameter ◽  
Impulsive Noise ◽  
Gaussian Kernel ◽  
Kernel Width ◽  
Optimal Value ◽  
Gradient Solution ◽  
Adaptive Kernel ◽  
Gradient Based ◽  
Complex Valued ◽  
Steady State Performance

Abstract The complex correntropy is a recently defined similarity measure that extends the advantages of conventional correntropy to complex-valued data. As in the real-valued case, the maximum complex correntropy criterion (MCCC) employs a free parameter called kernel width, which affects the convergence rate, robustness, and steady-state performance of the method. However, determining the optimal value for such parameter is not always a trivial task. Within this context, several works have introduced adaptive kernel width algorithms to deal with this free parameter, but such solutions must be updated to manipulate complex-valued data. This work reviews and updates the most recent adaptive kernel width algorithms so that they become capable of dealing with complex-valued data using the complex correntropy. Besides that, a novel gradient-based solution is introduced to the Gaussian kernel and its respective convergence analysis. Simulations compare the performance of adaptive kernel width algorithms with different fixed kernel sizes in an impulsive noise environment. The results show that the iterative kernel adjustment improves the performance of the gradient solution for complex-valued data.

scholarly journals An adaptive kernel width convex combination method for maximum correntropy criterion

2021 ◽  
Vol 27 (1) ◽  
Author(s):  
Aluisio I. R. Fontes ◽  
Leandro L. S. Linhares ◽  
João P. F. Guimarães ◽  
Luiz F. Q. Silveira ◽  
Allan M. Martins
Keyword(s):  
Adaptive Filtering ◽  
Convergence Rates ◽  
Impulsive Noise ◽  
Convex Combination ◽  
Combination Method ◽  
Kernel Width ◽  
Optimal Value ◽  
Adaptive Kernel ◽  
Novel Method ◽  
Maximum Correntropy Criterion

AbstractRecently, the maximum correntropy criterion (MCC) has been successfully applied in numerous applications regarding nonGaussian data processing. MCC employs a free parameter called kernel width, which affects the convergence rate, robustness, and steady-state performance of the adaptive filtering. However, determining the optimal value for such parameter is not always a trivial task. Within this context, this paper proposes a novel method called adaptive convex combination maximum correntropy criterion (ACCMCC), which combines an adaptive kernel algorithm with convex combination techniques. ACCMCC takes advantage from a convex combination of two adaptive MCC-based filters, whose kernel widths are adjusted iteratively as a function of the minimum error value obtained in a predefined estimation window. Results obtained in impulsive noise environment have shown that the proposed approach achieves equivalent convergence rates but with increased accuracy and robustness when compared with other similar algorithms reported in literature.

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