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Titlebook: Blind Signal Processing; Theory and Practice Xizhi Shi Book 2011 Shanghai Jiao Tong University Press,Shanghai and Springer-Verlag Berlin He

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https://doi.org/10.1007/978-3-322-91423-1ermann and Yang, 1997; Lee ., 1997; Lin and Cowan, 1997; Lin and Cowan, 1997; Table and Jutten, 1997; Hyvarinen and Oja, 1998; Hochreiter and Schemidhuber, 1998; Hyvarinen and Pajunen, 1999; Jutten, 2000; Jutten and Karhunen, 2003; Theis, 2003; Almeida, 2003, etc..
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Nonlinear ICA,ermann and Yang, 1997; Lee ., 1997; Lin and Cowan, 1997; Lin and Cowan, 1997; Table and Jutten, 1997; Hyvarinen and Oja, 1998; Hochreiter and Schemidhuber, 1998; Hyvarinen and Pajunen, 1999; Jutten, 2000; Jutten and Karhunen, 2003; Theis, 2003; Almeida, 2003, etc..
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https://doi.org/10.1007/978-3-322-91423-1The common method makes eigenvalues decomposition to the cumulant matrix that is estimated by the mixed signal samples. It can also perform joint diagonalization for the cumulant matrix through complex matrix transformation to estimate the mixing matrix in order to solve the BSS problem.
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Book 2011eld, such as probability density estimation-based processing algorithms, underdetermined models, complex value methods, uncertainty of order in the separation of convolutive mixtures in frequency domains, and feature extraction using Independent Component Analysis (ICA). At the end of the book, resu
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Joint Approximate Diagonalization Method, For infinite samples, JAD can calculate the common feature structure from any part of matrix set .. or linear combination of matrix in ..; but for finite samples ., the matrices in set .. can’t accurately share the same feature structure. The viewpoint of statistics, an “averaging” processing shoul
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Extension of Blind Signal Processing,cording to the characteristics of the signals to be extracted but can extract only a few signals, which can reduce the computing time, so the extraction algorithm is easier than the separation algorithm.
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