A general kernelization framework for learning algorithms based on kernel PCA

@article{Zhang2010AGK,
  title={A general kernelization framework for learning algorithms based on kernel PCA},
  author={Changshui Zhang and Feiping Nie and Shiming Xiang},
  journal={Neurocomputing},
  year={2010},
  volume={73},
  pages={959-967}
}
In this paper, a general kernelization framework for learning algorithms is proposed via a two-stage procedure, i.e., transforming data by kernel principal component analysis (KPCA), and then directly performing the learning algorithm with the transformed data. It is worth noting that although a very few learning algorithms were also kernelized by this procedure before, why and under what condition kernelization framework, and give a rigorous justification to reveal that under some mild… CONTINUE READING
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