Multivariate Gaussianization for Data Processing

Intro Iterative Gaussianization Experiments ConclusionsExperiment 3: One-class ClassificationBuilding the classifiersRBF kernel for the SVDD: K(x i , x j ) = exp ( −‖x i − x j ‖ 2 /2σ 2) , σ ∈ R + .Kernel width varied in σ ∈ [10 −2 , . . . , 10 2 ]The fraction rejection parameter was varied in ν ∈ [10 −2 , 0.5]Experimental setupTrain with a set of free parameters to maximize kappa statisticTraining sets of different size for the target class, [100, 2500]Test set was constituted by 10 5 pixelsExperiment repeated 10 runs in 3 images