Robust Kernel Approximation for Classification

@inproceedings{Liu2017RobustKA,
  title={Robust Kernel Approximation for Classification},
  author={Fanghui Liu and X. Huang and Cheng Peng and Jie Yang and Nikola K. Kasabov},
  booktitle={ICONIP},
  year={2017}
}
This paper investigates a robust kernel approximation scheme for support vector machine classification with indefinite kernels. It aims to tackle the issue that the indefinite kernel is contaminated by noises and outliers, i.e. a noisy observation of the true positive definite (PD) kernel. The traditional algorithms recovery the PD kernel from the observation with the small Gaussian noises, however, such way is not robust to noises and outliers that do not follow a Gaussian distribution. In… 
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