Blind Source Separation Based on Multiple Algorithm Fusion Using FSS-kernel and FastICA

DOI

10.2991/acta-14.2014.29How to use a DOI?

Keywords

FastICA algorithm, FSS-kernel algorithm, blind source separation, algorithm fusion.

Abstract

This paper presents a multiple algorithm fusion using FSS-kernel and FastICA to improve the accuracy of blind source separation. FastICA algorithm has a faster and more robust convergence speed than the traditional ICA algorithm. But its recovery results are not satisfied. Inspired by various successful applications of kernel and spectral clustering methods in machine learning and data mining community, probability density function of the source signal is estimated by the FSS-kernel algorithm, and then to restore the blind separation of mixed signals, FastICA algorithm is used, the negative entropy is the objective function. The simulation results show that the signal aliasing could be separated effecti- vely by this method. It is proved that the method has higher separation accuracy and adaptive capacity, by contrasting with the traditional ICA algorithms and FastICA algorithm.

Copyright

© 2014, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - He Pengju
AU  - Chou Xingxing
AU  - Chen Xiaomeng
AU  - Zeng Huazhi
PY  - 2014/06
DA  - 2014/06
TI  - Blind Source Separation Based on Multiple Algorithm Fusion Using FSS-kernel and FastICA
BT  - 2014 International Conference on Automatic Control Theory and Application
PB  - Atlantis Press
SP  - 119
EP  - 123
SN  - 2352-5398
UR  - https://doi.org/10.2991/acta-14.2014.29
DO  - 10.2991/acta-14.2014.29
ID  - Pengju2014/06
ER  -