Reformative nonlinear feature extraction using kernel MSE

@article{Zhu2010ReformativeNF,
  title={Reformative nonlinear feature extraction using kernel MSE},
  author={Qi Zhu},
  journal={Neurocomputing},
  year={2010},
  volume={73},
  pages={3334-3337}
}
In this paper, we propose an efficient nonlinear feature extraction method using kernel-based minimum squared error (KMSE). This improved method is referred to as reformative KMSE (RKMSE). In RKMSE, we use a linear combination of a small portion of samples that are selected from the training sample set, i.e. ‘‘significant nodes’’, to approximate to the transform vector of KMSE in kernel space. As a result, results on several benchmark datasets illustrate that RKMSE can efficiently classify the… CONTINUE READING
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