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  Taxonomy Inference Using Kernel Dependence Measures

Blaschko, M., & Gretton, A.(2008). Taxonomy Inference Using Kernel Dependence Measures (181). Tübingen, Germany: Max Planck Institute for Biological Cybernetics.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-C673-4 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-8702-3
Genre: Report

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MPIK-TR-181.pdf (Publisher version), 722KB
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 Creators:
Blaschko, MB1, 2, Author              
Gretton, A1, 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 introduce a family of unsupervised algorithms, numerical taxonomy clustering, to simultaneously cluster data, and to learn a taxonomy that encodes the relationship between the clusters. The algorithms work by maximizing the dependence between the taxonomy and the original data. The resulting taxonomy is a more informative visualization of complex data than simple clustering; in addition, taking into account the relations between different clusters is shown to substantially improve the quality of the clustering, when compared with state-of-the-art algorithms in the literature (both spectral clustering and a previous dependence maximization approach). We demonstrate our algorithm on image and text data.

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 Dates: 2008-11
 Publication Status: Published in print
 Pages: 11
 Publishing info: Tübingen, Germany : Max Planck Institute for Biological Cybernetics
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 Identifiers: Report Nr.: 181
BibTex Citekey: 5618
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Title: Technical Report of the Max Planck Institute for Biological Cybernetics
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Pages: - Volume / Issue: 181 Sequence Number: - Start / End Page: - Identifier: -