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• W2131672785 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." @default.
• W2131672785 created "2016-06-24" @default.
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• W2131672785 date "2005-12-05" @default.
• W2131672785 modified "2023-09-29" @default.
• W2131672785 title "Non-Local Manifold Parzen Windows" @default.
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