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  Manifold Denoising

Hein, M., & Maier, M. (2007). Manifold Denoising. Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference, 561-568.

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 Creators:
Hein, M1, Author           
Maier, M1, Author           
Schölkopf, Editor
B., Editor
Platt, J., Editor
Hofmann, T., Editor
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 Abstract: We consider the problem of denoising a noisily sampled submanifold M in R^d, where the submanifold M is a priori unknown and we are only given a noisy point sample. The presented denoising algorithm is based on a graph-based diffusion process of the point sample. We analyze this diffusion process using recent results about the convergence of graph Laplacians. In the experiments we show that our method is capable of dealing with non-trivial high-dimensional noise. Moreover using the denoising algorithm as pre-processing method we can improve the results of a semi-supervised learning algorithm.

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 Dates: 2007-09
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: ISBN: 0-262-19568-2
URI: http://nips.cc/Conferences/2006/
BibTex Citekey: 4249
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Title: Twentieth Annual Conference on Neural Information Processing Systems (NIPS 2006)
Place of Event: Vancouver, BC, Canada
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Title: Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 561 - 568 Identifier: -