Disturbance Grassmann Kernels for Subspace-Based Learning

@article{Hong2018DisturbanceGK,
  title={Disturbance Grassmann Kernels for Subspace-Based Learning},
  author={Junyuan Hong and Huanhuan Chen and Feng Lin},
  journal={Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
  year={2018}
}
  • Junyuan Hong, Huanhuan Chen, Feng Lin
  • Published 10 February 2018
  • Computer Science
  • Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
In this paper, we focus on subspace-based learning problems, where data elements are linear subspaces instead of vectors. To handle this kind of data, Grassmann kernels were proposed to measure the space structure and used with classifiers, e.g., Support Vector Machines (SVMs). However, the existing discriminative algorithms mostly ignore the instability of subspaces, which would cause the classifiers to be misled by disturbed instances. Thus we propose considering all potential disturbances of… 

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