Comparing distributions and shapes using the kernel distance

@inproceedings{Joshi2011ComparingDA,
  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},
  year={2011}
}
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
Highly Cited
This paper has 65 citations. REVIEW CITATIONS
27 Citations
12 References
Similar Papers

Citations

Publications citing this paper.

65 Citations

01020'11'13'15'17
Citations per Year
Semantic Scholar estimates that this publication has 65 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
Showing 1-10 of 12 references

Sharper bounds for Gaussian and emperical processes

  • M. Talagrand
  • Annals of Probability, 22:76
  • 1994
Highly Influential
4 Excerpts

Similar Papers

Loading similar papers…