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A kernel method for unsupervised structured network inference

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84763

Lippert,  C
Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84969

Stegle,  O
Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons75313

Borgwardt,  KM
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Lippert, C., Stegle, O., Ghahramani, Z., & Borgwardt, K. (2009). A kernel method for unsupervised structured network inference. Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AIStats 2009), 368-375.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-C539-1
Zusammenfassung
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.