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Distance-based network recovery under feature correlation

Adametz, David and Roth, Volker. (2014) Distance-based network recovery under feature correlation. In: Advances in neural information processing systems, 27. Cambridge (Mass.), pp. 775-783.

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Official URL: http://edoc.unibas.ch/dok/A6329089

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Abstract

We present an inference method for Gaussian graphical models when only pairwise distances of n objects are observed. Formally, this is a problem of estimating an n x n covariance matrix from the Mahalanobis distances dMH(xi, xj), where object xi lives in a latent feature space. We solve the problem in fully Bayesian fashion by integrating over the Matrix-Normal likelihood and a Matrix-Gamma prior; the resulting Matrix-T posterior enables network recovery even under strongly correlated features. Hereby, we generalize TiWnet, which assumes Euclidean distances with strict feature independence. In spite of the greatly increased flexibility, our model neither loses statistical power nor entails more computational cost. We argue that the extension is highly relevant as it yields significantly better results in both synthetic and real-world experiments, which is successfully demonstrated for a network of biological pathways in cancer patients.
Faculties and Departments:05 Faculty of Science > Departement Mathematik und Informatik > Informatik > Datenanalyse (Roth)
UniBasel Contributors:Roth, Volker and Adametz, David
Item Type:Book Section, refereed
Book Section Subtype:Further Contribution in a Book
Bibsysno:Link to catalogue
Publisher:MIT-Press
Note:Publication type according to Uni Basel Research Database: Book item
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Last Modified:07 Aug 2015 12:06
Deposited On:07 Aug 2015 12:06

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