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  A Hilbert-Schmidt Dependence Maximization Approach to Unsupervised Structure Discovery

Blaschko, M., & Gretton, A. (2008). A Hilbert-Schmidt Dependence Maximization Approach to Unsupervised Structure Discovery. In 6th International Workshop on Mining and Learning with Graphs (MLG 2008) (pp. 1-3).

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-C831-4 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-3D78-3
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: In recent work by (Song et al., 2007), it has been proposed to perform clustering by maximizing a Hilbert-Schmidt independence criterion with respect to a predefined cluster structure Y , by solving for the partition matrix, II. We extend this approach here to the case where the cluster structure Y is not fixed, but is a quantity to be optimized; and we use an independence criterion which has been shown to be more sensitive at small sample sizes (the Hilbert-Schmidt Normalized Information Criterion, or HSNIC, Fukumizu et al., 2008). We demonstrate the use of this framework in two scenarios. In the first, we adopt a cluster structure selection approach in which the HSNIC is used to select a structure from several candidates. In the second, we consider the case where we discover structure by directly optimizing Y.

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 Dates: 2008-07
 Publication Status: Published in print
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 Identifiers: BibTex Citekey: 5179
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Title: 6th International Workshop on Mining and Learning with Graphs (MLG 2008)
Place of Event: Helsinki, Finland
Start-/End Date: 2008-07-04 - 2008-07-05

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Title: 6th International Workshop on Mining and Learning with Graphs (MLG 2008)
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1 - 3 Identifier: -