Second-Order Approximation of Minimum Discrimination Information in Independent Component Analysis

Independent Component Analysis (ICA) is intended to recover the mutually independent sources from their linear mixtures, and FastICA is one of the most successful ICA algorithms. Although it seems reasonable to improve the performance of FastICA by introducing more nonlinear functions to the negentropy estimation, the original fixed-point method (approximate Newton method) in FastICA 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.

Automated summary

Independent Component Analysis (ICA) is a method to recover mutually independent sources from their linear mixtures. This study proposes a novel method based on the second-order approximation of minimum discrimination information (MDI) to improve the performance of FastICA algorithm when introducing more nonlinear functions.