Multi-level independent component analysis

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.