A kernel method for unsupervised structured network inference

Christoph Lippert, Oliver Stegle, Zoubin Ghahramani, Karsten Borgwardt
; Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics, PMLR 5:368-375, 2009.

Abstract

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.

Cite this Paper


BibTeX
@InProceedings{pmlr-v5-lippert09a, title = {A kernel method for unsupervised structured network inference}, author = {Christoph Lippert and Oliver Stegle and Zoubin Ghahramani and Karsten Borgwardt}, booktitle = {Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics}, pages = {368--375}, year = {2009}, editor = {David van Dyk and Max Welling}, volume = {5}, series = {Proceedings of Machine Learning Research}, address = {Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA}, month = {16--18 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v5/lippert09a/lippert09a.pdf}, url = {http://proceedings.mlr.press/v5/lippert09a.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T A kernel method for unsupervised structured network inference %A Christoph Lippert %A Oliver Stegle %A Zoubin Ghahramani %A Karsten Borgwardt %B Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2009 %E David van Dyk %E Max Welling %F pmlr-v5-lippert09a %I PMLR %J Proceedings of Machine Learning Research %P 368--375 %U http://proceedings.mlr.press %V 5 %W PMLR %X 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.
RIS
TY - CPAPER TI - A kernel method for unsupervised structured network inference AU - Christoph Lippert AU - Oliver Stegle AU - Zoubin Ghahramani AU - Karsten Borgwardt BT - Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics PY - 2009/04/15 DA - 2009/04/15 ED - David van Dyk ED - Max Welling ID - pmlr-v5-lippert09a PB - PMLR SP - 368 DP - PMLR EP - 375 L1 - http://proceedings.mlr.press/v5/lippert09a/lippert09a.pdf UR - http://proceedings.mlr.press/v5/lippert09a.html AB - 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. ER -
APA
Lippert, C., Stegle, O., Ghahramani, Z. & Borgwardt, K.. (2009). A kernel method for unsupervised structured network inference. Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics, in PMLR 5:368-375

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