Document Classification via Nonlinear Metric Learning

@article{Li2017DocumentCV,
  title={Document Classification via Nonlinear Metric Learning},
  author={Xin Li and Yanqin Bai and Siyun Zhou and Ying Li},
  journal={Neural Processing Letters},
  year={2017},
  pages={1-11}
}
Learning a proper distance metric is an important problem in document classification, because the similarities of samples in many problems are usually measured by distance metric. In this paper, we address the nonlinear metric leaning problem with applying in the document classification. First, we propose a new representation about nonlinear metric by using a linear combination of some basic kernels. Second, we give a linear metric learning method by a triplet constraint and k-nearest neighbors… CONTINUE READING