TY - GEN
T1 - An adaptive kernel width update for correntropy
AU - Zhao, Songlin
AU - Chen, Badong
AU - Príncipe, José C.
PY - 2012
Y1 - 2012
N2 - Correntropy, as an adaptive criterion of Information Theoretic Learning (ITL), has been successfully used in signal processing and machine learning. How to appropriately select the kernel width of correntropy is a crucial problem in correntropy applications. Existing kernel width selection methods are not suitable enough for this problem. In this paper, we develop an adaptive method for kernel width selection in correntropy. Based on the Middleton's non-Gaussian models, this method utilizes the kurtosis as a ratio to adjust the standard deviation of the prediction error to obtain the kernel width online. The superior performance of the new method has been demonstrated by simulation examples in the noisy frequency doubling and echo cancelation problems.
AB - Correntropy, as an adaptive criterion of Information Theoretic Learning (ITL), has been successfully used in signal processing and machine learning. How to appropriately select the kernel width of correntropy is a crucial problem in correntropy applications. Existing kernel width selection methods are not suitable enough for this problem. In this paper, we develop an adaptive method for kernel width selection in correntropy. Based on the Middleton's non-Gaussian models, this method utilizes the kurtosis as a ratio to adjust the standard deviation of the prediction error to obtain the kernel width online. The superior performance of the new method has been demonstrated by simulation examples in the noisy frequency doubling and echo cancelation problems.
UR - https://www.scopus.com/pages/publications/84865076485
U2 - 10.1109/IJCNN.2012.6252495
DO - 10.1109/IJCNN.2012.6252495
M3 - 会议稿件
AN - SCOPUS:84865076485
SN - 9781467314909
T3 - Proceedings of the International Joint Conference on Neural Networks
BT - 2012 International Joint Conference on Neural Networks, IJCNN 2012
T2 - 2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012
Y2 - 10 June 2012 through 15 June 2012
ER -