  Independent Component Analysis (ICA) is intended to recover the mutually
independent sources from their linear mixtures, and F astICA is one of the most
successful ICA algorithms. Although it seems reasonable to improve the
performance of F astICA by introducing more nonlinear functions to the
negentropy estimation, the original fixed-point method (approximate Newton
method) in F astICA degenerates under this circumstance. To alleviate this
problem, we propose a novel method based on the second-order approximation of
minimum discrimination information (MDI). The joint maximization in our method
is consisted of minimizing single weighted least squares and seeking unmixing
matrix by the fixed-point method. Experimental results validate its efficiency
compared with other popular ICA algorithms.
