Trimmed affine projection algorithms

Author

Badong Chen ; Xiaohan Yang ; Hong Ji ; Hua Qu ; Nanning Zheng ; Principe, Jose C.

Author_Institution

Sch. of Electron. & Inf. Eng., Xi´an Jiaotong Univ., Xi´an, China

fYear

2014

fDate

6-11 July 2014

Firstpage

1923

Lastpage

1928

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 filtering 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

adaptive filters; least squares approximations; regression analysis; KTAPA; LTS criterion; LTS estimation; TAPA; adaptive filtering; computational complexity; kernel trimmed affine projection algorithm; least trimmed squares estimation; trimmed affine projection algorithms; Convergence; Kernel; Noise; Projection algorithms; Robustness; Testing; Vectors; Least trimmed squares (LTS) estimator; affine projection algorithm (APA); kernel affine projection algorithm (KAPA);

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks (IJCNN), 2014 International Joint Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4799-6627-1

Type

conf

DOI

10.1109/IJCNN.2014.6889751

Filename

6889751