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Abstract: 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... (Update)
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BibTeX entry: (Update)
P. Vincent and Y. Bengio. Manifold parzen windows. In Advances in Neural Information Processing Systems 15, pages 825--832. MIT Press, 2003. http://citeseer.ist.psu.edu/vincent02manifold.html More
@misc{ vincent03manifold,
author = "P. Vincent and Y. Bengio",
title = "Manifold parzen windows",
text = "P. Vincent and Y. Bengio. Manifold parzen windows. In Advances in Neural
Information Processing Systems 15, pages 825--832. MIT Press, 2003.",
year = "2003",
url = "citeseer.ist.psu.edu/vincent02manifold.html" }
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