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Deterministic Decoupling of Global Features for Data Analysis
  • Eduardo Martínez-Enríquez ,
  • Maria del Mar González ,
  • Javier Portilla
Eduardo Martínez-Enríquez
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Maria del Mar González
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Javier Portilla
Spanish Scientific Research Council (CSIC)

Corresponding Author:[email protected]

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Abstract

We introduce a method for deterministic decoupling of global features with application to data analysis. We propose a new formalism that is based on defining transformations on submanifolds, by following trajectories along the features’ gradients. Through these transformations, we define a type of normalization that, we demonstrate, allows for decoupling differentiable features. We apply this to sampling moments, obtaining a quasi-analytic solution for the orthokurtosis, a normalized version of the kurtosis that is not just decoupled from mean and variance, but also from skewness. We also apply the proposed method, up to sixth-order moments, in the original data domain and at the output of a filter bank, to regression and texture classification problems, consistently obtaining strong improvements in performance as compared to using classical (non-decoupled) features