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  Learning Taxonomies by Dependence Maximization

Blaschko, M., & Gretton, A. (2009). Learning Taxonomies by Dependence Maximization. In D. Schuurmans, Y. Bengio, & L. Bottou (Eds.), Advances in neural information processing systems 21 (pp. 153-160). Red Hook, NY, USA: Curran.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-C48B-F Version Permalink: http://hdl.handle.net/21.11116/0000-0002-DE60-8
Genre: Conference Paper

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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-ofthe-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: 2009-06
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 5396
 Degree: -

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Title: Twenty-Second Annual Conference on Neural Information Processing Systems (NIPS 2008)
Place of Event: Vancouver, BC, Canada
Start-/End Date: 2008-12-08 - 2008-12-10

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Title: Advances in neural information processing systems 21
Source Genre: Proceedings
 Creator(s):
Koller, D, Painter
Schuurmans, D, Editor
Bengio, Y, Editor
Bottou, L, Editor
Affiliations:
-
Publ. Info: Red Hook, NY, USA : Curran
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 153 - 160 Identifier: ISBN: 978-1-60560-949-2