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• W3217551419 abstract "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." @default.
• W3217551419 created "2021-12-06" @default.
• W3217551419 creator A5030103532 @default.
• W3217551419 date "2022-01-01" @default.
• W3217551419 modified "2023-09-26" @default.
• W3217551419 title "Second-Order Approximation of Minimum Discrimination Information in Independent Component Analysis" @default.
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• W3217551419 doi "https://doi.org/10.1109/lsp.2021.3135193" @default.
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