Design of new kernel density estimator for entropy maximization in independent component analysis

This paper proposes a new algorithm for estimating the score function using maximum entropy theory and kernel density estimation. The main idea is to control smoothing parameters for maximizing entropy in kernel density estimation. To generate score function, directly partial derivative equation from kernel density estimator is derived. To find suitable smoothing parameter, we adopted gradient descent method. Finally, the new kernel density estimator is experimented in blind separation and discuss on properties of the proposed learning algorithm.