TY - GEN
T1 - Hybrid affine projection algorithm
AU - Yang, Xiaohan
AU - Qu, Hua
AU - Zhao, Jihong
AU - Chen, Badong
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014
Y1 - 2014
N2 - In this work, we put forward a new adaptation criterion, namely the hybrid criterion (HC), which is a mixture of the traditional mean square error (MSE) and the maximum correntropy criterion (MCC). The HC criterion is developed from the viewpoint of the least trimmed squares (LTS) estimator, a high breakdown estimator that can avoid undue influence from outliers. In the LTS estimator, the data are divided (by ranking) into two categories: the normal data and the outliers, and the outlier data are purely discarded. In order to improve the robustness of the LTS, some data with large values, which may contain some useful information, are also thrown away. Instead of purely throwing away those data, the new criterion applies the robust MCC criterion on the large data, and hence can efficiently utilize them to further improve the performance. We apply the HC criterion to adaptive filtering and develop the hybrid affine projection algorithm (HAPA) and kernel hybrid affine projection algorithm (KHAPA). Simulation results show that the proposed algorithms perform very well.
AB - In this work, we put forward a new adaptation criterion, namely the hybrid criterion (HC), which is a mixture of the traditional mean square error (MSE) and the maximum correntropy criterion (MCC). The HC criterion is developed from the viewpoint of the least trimmed squares (LTS) estimator, a high breakdown estimator that can avoid undue influence from outliers. In the LTS estimator, the data are divided (by ranking) into two categories: the normal data and the outliers, and the outlier data are purely discarded. In order to improve the robustness of the LTS, some data with large values, which may contain some useful information, are also thrown away. Instead of purely throwing away those data, the new criterion applies the robust MCC criterion on the large data, and hence can efficiently utilize them to further improve the performance. We apply the HC criterion to adaptive filtering and develop the hybrid affine projection algorithm (HAPA) and kernel hybrid affine projection algorithm (KHAPA). Simulation results show that the proposed algorithms perform very well.
KW - Hybrid criterion (HC)
KW - Least trimmed squares (LTS)
KW - affine projection algorithm (APA)
KW - kernel adaptive filtering
KW - maximum correntropy criterion (MCC)
UR - https://www.scopus.com/pages/publications/84988301827
U2 - 10.1109/ICARCV.2014.7064436
DO - 10.1109/ICARCV.2014.7064436
M3 - 会议稿件
AN - SCOPUS:84988301827
T3 - 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014
SP - 964
EP - 968
BT - 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014
Y2 - 10 December 2014 through 12 December 2014
ER -