An Algorithm of Score Function Generation using Convolution-FFT in Independent Component Analysis


The KIPS Transactions:PartB , Vol. 13, No. 1, pp. 27-34, Feb. 2006
10.3745/KIPSTB.2006.13.1.27,   PDF Download:

Abstract

In this study, we propose this new algorithm that generates score function in ICA(Independent Component Analysis) using entropy theory. To generate score function, estimation of probability density function about original signals are certainly necessary and density function should be differentiated. Therefore, we used kernel density estimation method in order to derive differential equation of score function by original signal. After changing formula to convolution form to increase speed of density estimation, we used FFT algorithm that can calculate convolution faster. Proposed score function generation method reduces the errors, it is density difference of recovered signals and originals signals. In the result of computer simulation, we estimate density function more similar to original signals compared with Extended Infomax and Fixed Point ICA in blind source separation problem and get improved performance at the SNR(Signal to Noise Ratio) between recovered signals and original signal.


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Cite this article
[IEEE Style]
W. M. Kim and H. S. Lee, "An Algorithm of Score Function Generation using Convolution-FFT in Independent Component Analysis," The KIPS Transactions:PartB , vol. 13, no. 1, pp. 27-34, 2006. DOI: 10.3745/KIPSTB.2006.13.1.27.

[ACM Style]
Woong Myung Kim and Hyon Soo Lee. 2006. An Algorithm of Score Function Generation using Convolution-FFT in Independent Component Analysis. The KIPS Transactions:PartB , 13, 1, (2006), 27-34. DOI: 10.3745/KIPSTB.2006.13.1.27.