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http://dx.doi.org/10.3745/KIPSTB.2006.13B.1.027

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

Kim Woong-Myung (경희대학교 컴퓨터공학과)
Lee Hyon-Soo (경희대학교 컴퓨터공학과)
Publication Information
The KIPS Transactions:PartB / v.13B, no.1, 2006 , pp. 27-34 More about this Journal
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.
Keywords
Independent Component Analysis; Kernel Density Estimation; Convolution; FFT; Source Separation; Entropy;
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