Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Efficient source adaptivity in independent component analysis
IEEE Transactions on Neural Networks
Independent component analysis based on nonparametric density estimation
IEEE Transactions on Neural Networks
Multi-level independent component analysis
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
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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 signals. After changing the formula to convolution form to increase speed of density estimation, we used FFT algorithm which calculates convolution faster. Proposed score function generation method reduces estimation error, it is density difference of recovered signals and original signals. Also, we insert constraint which is able to information maximization using smoothing parameters. In the result of computer simulation, we estimate density function more similar to original signals compared with Extended Infomax algorithm 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 signals.