Multivariate Gaussianization for Data Processing

Intro Iterative Gaussianization Experiments ConclusionsExperiment 1: Density estimation toy examplesToy exampleTheoretical PDF Scatter plot Histogram G-PCA PDFe 3. Example of 10PDF 2 samples estimation. usedFrom left to right: theoretical PDF, scatter plot of the data used in the estimaram estimation Much usingsmoother a number of estimation bins to obtain the same resolution as in the G-PCA estimation.Problems and limitations 5. EXPERIMENTAL RESULTSroposed method We need is illustrated to ensure in two connected experiments supports and will forbe the compared PDF to standard SVDD because ofive similarity (cf. Section 3.1). The first 2D experiment on synthetic data illustrates the capabilities ood in non-linearly For clustered separabledata, and badly estimate sampled individual one-class transforms problems. for The each second cluster experiment deals withspectral andThe multisource Jacobian data estimation and illustrates is highly the advantages point-dependent of G-PCA in real and challenging scenarExperiment 1: 2D non-linearly separable problems.is 2D experiment the problem is detecting outliers from the target class represented by dots in Fig. 4.