Multi-level independent component analysis

Authors:
Woong Myung Kim;Chan Ho Park;Hyon Soo Lee
Affiliations:
Dept. of Computer Engineering, Kyunghee University, Gyeonggi-do, Republic of Korea;Dept. of Internet Information Science, Bucheon College, Gyeonggi-do, Republic of Korea;Dept. of Computer Engineering, Kyunghee University, Gyeonggi-do, Republic of Korea
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Year:
2006

Citing 7
Cited 0

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Abstract

This paper presents a new method which uses multi-level density estimation technique to generate score function in ICA (independent Component Analysis). Score function is very closely related with density function in information theoretic ICA. We tried to solve mismatch of marginal densities by controlling the number of kernels. Also, we insert a constraint that can satisfy sufficient condition to guarantee asymptotic stability. Multi-level ICA uses kernel density estimation method in order to derive differential equation of source adaptively score function by original signals. To increase speed of kernel density estimation, we used FFT algorithm after changing density formula to convolution form. Proposed multi-level score function generation method reduces estimate error which is density difference between recovered signals and original signals. We estimate density function more similar to original signals compared with existent other algorithms in blind source separation problem and get improved performance in the SNR measurement.