Nonlinear Blind Source Separation Using Kernel Multi-Set Canonical Correlation Analysis
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 non-linearity. 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.