Manifold Parzen Windows

@inproceedings{Vincent2002ManifoldPW,
  title={Manifold Parzen Windows},
  author={Pascal Vincent and Yoshua Bengio},
  booktitle={NIPS},
  year={2002}
}
The similarity between objects is a fundamental element of many learning algorithms. Most non-parametric methods take this similarity to be fixed, but much recent work has shown the advantages of learning it, in particular to exploit the local invariances in the data or to capture the possibly non-linear manifold on which most of the data lies. We propose a new non-parametric kernel density estimation method which captures the local structure of an underlying manifold through the leading… CONTINUE READING
76 Citations
21 References
Similar Papers

Citations

Publications citing this paper.

References

Publications referenced by this paper.
Showing 1-10 of 21 references

Brand . Charting a manifold

  • M.
  • 2003

Global versus local approaches to nonlinear dimensionality reduction

  • V. de Silva, J. B. Tenenbaum
  • Advances in Neural Information Processing Systems…
  • 2003
1 Excerpt

A probabilistic view onta gent distance

  • J. Dahmen D. Keysers, H. Ney.
  • 2000

Similar Papers

Loading similar papers…