Nonlinear Blind Source Separation Using Kernel Multi-set Canonical Correlation Analysis
Автор: Hua-Gang Yu, Gao-Ming Huang, Jun Gao
Журнал: International Journal of Computer Network and Information Security(IJCNIS) @ijcnis
Статья в выпуске: 1 vol.2, 2010 года.
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To solve the problem of nonlinear blind source separation (BSS), a novel algorithm based on kernel multi-set canonical correlation analysis (MCCA) is presented. Combining complementary research fields of kernel feature spaces and BSS using MCCA, the proposed approach yields a highly efficient and elegant algorithm for nonlinear BSS with invertible nonlinearity. The algorithm works as follows: First, the input data is mapped to a high-dimensional feature space and perform dimension reduction to extract the effective reduced feature space, translate the nonlinear problem in the input space to a linear problem in reduced feature space. In the second step, the MCCA algorithm was used to obtain the original signals.
Nonlinear blind source separation, kernel feature spaces, multi-set canonical correlation analysis, reduced feature space, joint diagonalization
Короткий адрес: https://sciup.org/15010979
IDR: 15010979
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