Generalized Discriminant Analysis Using a Kernel Approach

@article{Baudat2000GeneralizedDA,
  title={Generalized Discriminant Analysis Using a Kernel Approach},
  author={G. Baudat and Fatiha Anouar},
  journal={Neural Computation},
  year={2000},
  volume={12},
  pages={2385-2404}
}
We present a new method that we call generalized discriminant analysis (GDA) to deal with nonlinear discriminant analysis using kernel function operator. The underlying theory is close to the support vector machines (SVM) insofar as the GDA method provides a mapping of the input vectors into high-dimensional feature space. In the transformed space, linear properties make it easy to extend and generalize the classical linear discriminant analysis (LDA) to nonlinear discriminant analysis. The… CONTINUE READING
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