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 2: <strong>Data</strong> synthesis<strong>Data</strong> synthesis 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 its Jacobian, J G4: Sample randomly in the Gaussianized domain5: Trans<strong>for</strong>m back to the original domainAdvantagesRobustness to high dimensional problemsNo data distribution assumptions, no parametric model eitherLow computational cost
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