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International Conference on Mechanical and Electrical Technology, 3rd, (ICMET-China 2011), Volumes 1–3

By
Yi Xie
Yi Xie
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ISBN:
9780791859810
No. of Pages:
906
Publisher:
ASME Press
Publication date:
2011

Kernel Independent Component Analysis (KICA) is a non-linear method for blind source separation (BSS) advanced recently. It has a high computational complexity. Recurring to the characteristic of dimension reduction and redundancy removing of kernel entropy component analysis (KECA), an improved algorithm of KICA, kernel entropy independent component analysis (KEICA), is proposed to reduce the high computational complexity of KICA. In a simulation experiment with the separation performance, Amari error and the accumulated time as the criterions, KEICA is compared to KICA. Results show that KEICA is superior to KICA comprehensively.

Abstract
Key Words
1 Introduction
2 Basic Theory
3 The Improved Kica Algorithm: Keica
4 Simulation Experiment
5 Summaries
6 Acknowledgments
References
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