Journal
IEEE SIGNAL PROCESSING LETTERS
Volume 29, Issue -, Pages 334-338Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LSP.2021.3135193
Keywords
Independent component analysis; minimum discrimination information; second-order approximation; FastICA; weighted least squares
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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.
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.
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