Titlebook: Latent Variable Analysis and Signal Separation; 10th International C Fabian Theis,Andrzej Cichocki,Michael Zibulevsky Conference proceeding

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Anal-Canal

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A Probability-Based Combination Method for Unsupervised Clustering with Application to Blind Source ombining the results of these clustering methods the corresponding clusters have to be aligned, but usually it is not known which clusters of the employed methods correspond to each other. In this paper, we present a method to avoid this correspondence problem using probability theory. We also prese

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Charrelation Matrix Based ICAenient 2-dimensional structure. In the context of ICA, charrelation matrices-based separation was recently shown to potentially attain superior performance over commonly used methods. However, this approach is strongly dependent on proper selection of the parameters (termed .) which parameterize the

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Contrast Functions for Independent Subspace Analysislly solve the ISA problem. That is, basic ICA can solve the ISA problem up to within-subspace separation/analysis. We define sub- and super-Gaussian subspaces and extend to ISA a previous result on freedom of ICA from local optima. We also consider new types of dependent densities that satisfy or vi

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