Out-of-Sample Extensions for Non-Parametric Kernel Methods

@article{Pan2017OutofSampleEF,
  title={Out-of-Sample Extensions for Non-Parametric Kernel Methods},
  author={Binbin Pan and Wensheng Chen and Bo Chen and Chen Xu and Jianhuang Lai},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
  year={2017},
  volume={28},
  pages={334-345}
}
Choosing suitable kernels plays an important role in the performance of kernel methods. Recently, a number of studies were devoted to developing nonparametric kernels. Without assuming any parametric form of the target kernel, nonparametric kernel learning offers a flexible scheme to utilize the information of the data, which may potentially characterize the data similarity better. The kernel methods using nonparametric kernels are referred to as nonparametric kernel methods. However, many… CONTINUE READING

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