Nonlinear Discriminant Principal Component Analysis for Image Classification and Reconstruction

@article{Filisbino2018NonlinearDP,
  title={Nonlinear Discriminant Principal Component Analysis for Image Classification and Reconstruction},
  author={Tiene A. Filisbino and Gilson A. Giraldi and Carlos E. Thomaz},
  journal={2018 7th Brazilian Conference on Intelligent Systems (BRACIS)},
  year={2018},
  pages={312-317}
}
In this paper we present a nonlinear version of the discriminant principal component analysis, named NDPCA, that is based on kernel support vector machines (KSVM) and the AdaBoost technique. Specifically, the problem of ranking principal components, computed from two-class databases, is addressed by applying the AdaBoost procedure in a nested loop: each iteration of the inner loop boosts weak classifiers to a moderate one while the outer loop combines the moderate classifiers to build the… CONTINUE READING

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