@inproceedings{f3d01ade53f94a79bc198825d880f513,
title = "Trimmed affine projection algorithms",
abstract = "The least trimmed squares (LTS) estimator is a robust estimator as it can avoid undue influence from outliers. The exact solution of the LTS estimation is however hard to And and if the number of data is large then the method is unfeasible. In this work, we apply the LTS criterion to adaptive Altering and develop the trimmed affine projection algorithm (TAPA) and kernel trimmed affine projection algorithm (KTAPA). The proposed adaptive algorithms are very robust to outliers and have low computational complexity. Simulation results conflrm their excellent and robust performance.",
keywords = "Least trimmed squares (LTS) estimator, affine projection algorithm (APA), kernel affine projection algorithm (KAPA)",
author = "Badong Chen and Xiaohan Yang and Hong Ji and Hua Qu and Nanning Zheng and Principe, \{Jose C.\}",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 International Joint Conference on Neural Networks, IJCNN 2014 ; Conference date: 06-07-2014 Through 11-07-2014",
year = "2014",
month = sep,
day = "3",
doi = "10.1109/IJCNN.2014.6889751",
language = "英语",
series = "Proceedings of the International Joint Conference on Neural Networks",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1923--1928",
booktitle = "Proceedings of the International Joint Conference on Neural Networks",
}