Comparing distributions and shapes using the kernel distance

  title={Comparing distributions and shapes using the kernel distance},
  author={Sarang C. Joshi and Raj Varma Kommaraju and Jeff M. Phillips and Suresh Venkatasubramanian},
  booktitle={Symposium on Computational Geometry},
Starting with a similarity function between objects, it is possible to define a distance metric (the kernel distance) on pairs of objects, and more generally on probability distributions over them. These distance metrics have a deep basis in functional analysis and geometric measure theory, and have a rich structure that includes an isometric embedding into a Hilbert space. They have recently been applied to numerous problems in machine learning and shape analysis. SIn this paper, we provide… CONTINUE READING
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Sharper bounds for Gaussian and emperical processes

  • M. Talagrand
  • Annals of Probability, 22:76
  • 1994
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