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          Institute: MPI für biologische Kybernetik     Collection: Biologische Kybernetik     Display Documents



ID: 461791.0, MPI für biologische Kybernetik / Biologische Kybernetik
A kernel method for unsupervised structured network inference
Authors:Lippert, C.; Stegle, O.; Ghahramani, Z.; Borgwardt, K.M.
Editors:Dyk, D. Van; Welling, M.
Date of Publication (YYYY-MM-DD):2009-04
Title of Proceedings:Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AIStats 2009)
Start Page:368
End Page:375
Physical Description:8
Audience:Not Specified
Intended Educational Use:No
Abstract / Description:Network inference is the problem of inferring edges between a set of real-world objects, for instance, interactions between pairs of proteins in bioinformatics. Current kernel-based approaches to this problem share a set of common features: (i) they are supervised and hence require labeled training data; (ii) edges in the network are treated as mutually independent and hence topological properties are largely ignored; (iii) they lack a statistical interpretation. We argue that these common assumptions are often undesirable for network inference, and propose (i) an unsupervised kernel method (ii) that takes the global structure of the network into account and (iii) is statistically motivated. We show that our approach can explain commonly used heuristics in statistical terms. In experiments on social networks, different variants of our method demonstrate appealing predictive performance.
External Publication Status:published
Document Type:Conference-Paper
Communicated by:Holger Fischer
Affiliations:MPI für biologische Kybernetik/Empirical Inference (Dept. Schölkopf)
Identifiers:LOCALID:5663
URL:http://www.ics.uci.edu/~aistats/index.html
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