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  A Local Learning Approach for Clustering

Wu, M., & Schölkopf, B. (2007). A Local Learning Approach for Clustering. In B. Schölkopf, J. Platt, & T. Hoffman (Eds.), Advances in Neural Information Processing Systems 19 (pp. 1529-1536). Cambridge, MA, USA: MIT Press.

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 Creators:
Wu, M1, 2, Author           
Schölkopf, B1, 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: We present a local learning approach for clustering. The basic idea is that a good clustering result should have the property that the cluster label of each data point can be well predicted based on its neighboring data and their cluster labels, using current supervised learning methods. An optimization problem is formulated such that its solution has the above property. Relaxation and eigen-decomposition are applied to solve this optimization problem. We also briefly investigate the parameter selection issue and provide a simple parameter selection method for the proposed algorithm. Experimental results are provided to validate the effectiveness of the proposed approach.

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 Dates: 2007-09
 Publication Status: Issued
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 4149
 Degree: -

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Title: Twentieth Annual Conference on Neural Information Processing Systems (NIPS 2006)
Place of Event: Vancouver, BC, Canada
Start-/End Date: 2006-12-04 - 2006-12-07

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Title: Advances in Neural Information Processing Systems 19
Source Genre: Proceedings
 Creator(s):
Schölkopf, B1, Editor           
Platt, JC, Editor
Hoffman, T, Editor
Affiliations:
1 Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795            
Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1529 - 1536 Identifier: ISBN: 0-262-19568-2