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  Manifold Denoising as Preprocessing for Finding Natural Representations of Data

Hein, M., & Maier, M. (2007). Manifold Denoising as Preprocessing for Finding Natural Representations of Data. In Twenty-Second AAAI Conference on Artificial Intelligence (AAAI-07) (pp. 1646-1649). Menlo Park, CA, USA: AAAI Press.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-CCBF-8 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-E130-8
Genre: Conference Paper

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 Creators:
Hein, M1, 2, Author              
Maier, M1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: A natural representation of data are the parameters which generated the data. If the parameter space is continuous we can regard it as a manifold. In practice we usually do not know this manifold but we just have some representation of the data, often in a very high-dimensional feature space. Since the number of internal parameters does not change with the representation, the data will effectively lie on a low-dimensional submanifold in feature space. Due to measurement errors this data is usually corrupted by noise which particularly in high-dimensional feature spaces makes it almost impossible to find the manifold structure. This paper reviews a method called Manifold Denoising which projects the data onto the submanifold using a diffusion process on a graph generated by the data. We will demonstrate that the method is capable of dealing with non-trival high-dimensional noise. Moreover we will show that using the method as a preprocessing step one can significantly improve the results of a semi-supervised learning algorithm.

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 Dates: 2007-07
 Publication Status: Published in print
 Pages: -
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 Rev. Type: -
 Identifiers: BibTex Citekey: 4588
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Title: Twenty-Second AAAI Conference on Artificial Intelligence (AAAI-07)
Place of Event: Vancouver, BC, Canada
Start-/End Date: 2007-07-22 - 2007-07-26

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Title: Twenty-Second AAAI Conference on Artificial Intelligence (AAAI-07)
Source Genre: Proceedings
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Publ. Info: Menlo Park, CA, USA : AAAI Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1646 - 1649 Identifier: ISBN: 978-1-57735-323-2