TY - GEN
T1 - Kernel robust mixed-norm adaptive filtering
AU - Liu, Jin
AU - Qu, Hua
AU - Chen, Badong
AU - Ma, Wentao
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/9/3
Y1 - 2014/9/3
N2 - Kernel methods are powerful for developing a nonlinear learning algorithm in a high-dimensional linear space. The least mean square (LMS) and the least absolute deviation (LAD) are two well-known linear adaptive filtering algorithms. The former performs very well when the noise is Gaussian, while the later possesses desirable performance when the noise has a long-tailed distribution (e.g. alpha-stable distribution). The combination of the LMS and LAD yields a robust mixed-norm (RMN) algorithm. In this paper, we combine the popular kernel methods and the RMN algorithm to develop a new kernel adaptive filtering algorithm, namely the kernel RMN (KRMN) algorithm, which is a robust adaptive algorithm in reproducing kernel Hilbert space (RIOTS). The mean square convergence is analyzed, and the excellent and robust performance of the new algorithm is demonstrated by the simulation results of nonlinear time series prediction.
AB - Kernel methods are powerful for developing a nonlinear learning algorithm in a high-dimensional linear space. The least mean square (LMS) and the least absolute deviation (LAD) are two well-known linear adaptive filtering algorithms. The former performs very well when the noise is Gaussian, while the later possesses desirable performance when the noise has a long-tailed distribution (e.g. alpha-stable distribution). The combination of the LMS and LAD yields a robust mixed-norm (RMN) algorithm. In this paper, we combine the popular kernel methods and the RMN algorithm to develop a new kernel adaptive filtering algorithm, namely the kernel RMN (KRMN) algorithm, which is a robust adaptive algorithm in reproducing kernel Hilbert space (RIOTS). The mean square convergence is analyzed, and the excellent and robust performance of the new algorithm is demonstrated by the simulation results of nonlinear time series prediction.
KW - kernel
KW - mixed-norm
KW - robust adaptive filtering
UR - https://www.scopus.com/pages/publications/84908479845
U2 - 10.1109/IJCNN.2014.6889889
DO - 10.1109/IJCNN.2014.6889889
M3 - 会议稿件
AN - SCOPUS:84908479845
T3 - Proceedings of the International Joint Conference on Neural Networks
SP - 3021
EP - 3024
BT - Proceedings of the International Joint Conference on Neural Networks
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 International Joint Conference on Neural Networks, IJCNN 2014
Y2 - 6 July 2014 through 11 July 2014
ER -