FASTICA MATLAB代码 经典文献

The FastICA algorithm [This is probably the most widely used algorithm for performing independent component analysis, a recently developed variant of factor analysis that is completely estimable unlike classical methods, and able to perform blind source separation] FastICA package for Matlab and other systems A. Hyv?rinen. Fast and Robust Fixed-Point Algorithms for Independent Component Analysis. IEEE Transactions on Neural Networks 10(3):626-634, 1999. Postscript gzipped PostScript pdf [The fundamental paper on the FastICA algorithm, which is computationally very efficient yet statistically robust. This article is based on the TechRep "ICA by Minimization of Mutual Information".] A. Hyv?rinen and E. Oja. Independent Component Analysis: Algorithms and Applications. Neural Networks, 13(4-5):411-430, 2000. html (and Japanese version) Postscript gzipped PostScript pdf [A tutorial text on ICA in general, and FastICA in particular.] A. Hyv?rinen and E. Oja. A Fast Fixed-Point Algorithm for Independent Component Analysis. Neural Computation, 9(7):1483-1492, 1997. Postscript gzipped PostScript pdf. [Introduced the original, cumulant-based, form of the FastICA algorithm.] A. Hyv?rinen. New Approximations of Differential Entropy for Independent Component Analysis and Projection Pursuit. In Advances in Neural Information Processing Systems 10 (NIPS*97), pp. 273-279, MIT Press, 1998. Postscript gzipped PostScript pdf. Related TechRep: Postscript gzipped PostScript. pdf. [Introduced approximations of differential entropy used in the derivation of the FastICA algorithm in the IEEE Transactions paper above.] E. Bingham and A. Hyv?rinen A fast fixed-point algorithm for independent component analysis of complex-valued signals. Int. J. of Neural Systems, 10(1):1-8, 2000. Postscript gzipped PostScript pdf Matlab code [A version of FastICA for complex-valued data.]