Non-local manifold parzen windows

Non-local manifold parzen windows (2006)

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by Yoshua Bengio , Hugo Larochelle , Pascal Vincent

Venue:	In Advances in Neural Information Processing Systems 18
Citations:	14 - 7 self

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@INPROCEEDINGS{Bengio06non-localmanifold,
    author = {Yoshua Bengio and Hugo Larochelle and Pascal Vincent},
    title = {Non-local manifold parzen windows},
    booktitle = {In Advances in Neural Information Processing Systems 18},
    year = {2006},
    pages = {115--122},
    publisher = {MIT Press}
}

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Abstract

To escape from the curse of dimensionality, we claim that one can learn non-local functions, in the sense that the value and shape of the learned function at x must be inferred using examples that may be far from x. With this objective, we present a non-local non-parametric density estimator. It builds upon previously proposed Gaussian mixture models with regularized covariance matrices to take into account the local shape of the manifold. It also builds upon recent work on non-local estimators of the tangent plane of a manifold, which are able to generalize in places with little training data, unlike traditional, local, non-parametric models. 1

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