Learning Euclidean-to-Riemannian Metric for Point-to-Set Classification

@article{Huang2014LearningEM,
  title={Learning Euclidean-to-Riemannian Metric for Point-to-Set Classification},
  author={Zhiwu Huang and Ruiping Wang and Shiguang Shan and Xilin Chen},
  journal={2014 IEEE Conference on Computer Vision and Pattern Recognition},
  year={2014},
  pages={1677-1684}
}
In this paper, we focus on the problem of point-to-set classification, where single points are matched against sets of correlated points. Since the points commonly lie in Euclidean space while the sets are typically modeled as elements on Riemannian manifold, they can be treated as Euclidean points and Riemannian points respectively. To learn a metric between the heterogeneous points, we propose a novel Euclidean-to-Riemannian metric learning framework. Specifically, by exploiting typical… CONTINUE READING
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