Multivariate Gaussianization for Data Processing
Multivariate Gaussianization for Data Processing
Multivariate Gaussianization for Data Processing
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Intro Iterative <strong>Gaussianization</strong> Experiments ConclusionsExperiment 5: Multi-in<strong>for</strong>mation estimationMeasuring independence with RBIG1: Input: Given data x (0) = [x 1, . . . , x d ] ⊤ ∈ R d2: Learn the sequence of <strong>Gaussianization</strong> trans<strong>for</strong>ms, G, such that y = G(x)3: Compute the cumulative reduction in mutual in<strong>for</strong>mationAdvantagesRobustness to high dimensional problems, no need of histograms!No data distribution assumptions, no parametric model eitherLow computational cost
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