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Conference Paper

Partially-supervised context-specific independence mixture modeling.

MPS-Authors

Georgi,  Benjamin
Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons50523

Schliep,  Alexander
Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Fulltext (public)

Georgi2007c.pdf
(Any fulltext), 215KB

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Citation

Georgi, B., & Schliep, A. (n.d.). Partially-supervised context-specific independence mixture modeling. In P.-O.-T.-S.-I.-W.-O.-M.-R.-R.-D. MINING (Ed.), ECML. Berlin/Heidelberg: Springer.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0010-816E-D
Abstract
Partially supervised or semi-supervised learning refers to machine learning methods which fall between clustering and classification. In the context of clustering, labels can specify link and do-not-link constraints between data points in di erent ways and constrain the resulting clustering solutions. This is a very natural framework for many biological applications as some labels are often available and even very few label greatly improve clustering results. Context-specific independence models constitute a framework for simultaneous mixture estimation and model structure determination to obtain meaningful models for high-dimensional data with many, possibly uninformative, variables. Here we present the first approach for partial learning of CSI models and demonstrate the e ectiveness of modest amounts of labels for simulated data and for protein sub-family determination.