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 dependence‘Two random variables are independent if the conditional probabilitydistribution of either given the observed value of the other is the same as if theother’s value had not been observed’ApplicationsFeature selection<strong>Data</strong> analysis<strong>Data</strong> codingMany MethodsCorrelation only measures linear dependencesNon-linear extensions availableKernel methods can estimate higher-order dependencesMutual in<strong>for</strong>mation measures dependence between two r.v.’sMulti-in<strong>for</strong>mation generalizes mutual in<strong>for</strong>mation
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