Efficient inference in matrix-variate Gaussian models with iid observation noise

Stegle, O; Lippert, C; Mooij, J; Lawrence, N; Borgwardt, K

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Efficient inference in matrix-variate Gaussian models with iid observation noise

Stegle, O., Lippert, C., Mooij, J., Lawrence, N., & Borgwardt, K. (2012). Efficient inference in matrix-variate Gaussian models with iid observation noise. In J. Shawe-Taylor, R. Zemel, P. Bartlett, F. Pereira, & K. Weinberger (Eds.), Advances in Neural Information Processing Systems 24 (pp. 630-638). Red Hook, NY, USA: Curran.

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Item Permalink: https://hdl.handle.net/21.11116/0000-000A-5801-2 Version Permalink: https://hdl.handle.net/21.11116/0000-0010-5AFB-F

Genre: Conference Paper

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https://proceedings.neurips.cc/paper/2011/file/a732804c8566fc8f498947ea59a841f8-Paper.pdf (Publisher version) Open Access status unknown

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Creators:
Stegle, O¹, Author
Lippert, C¹, Author
Mooij, J, Author
Lawrence, N, Author
Borgwardt, K¹, Author

Affiliations:
1Department Molecular Biology, Max Planck Institute for Developmental Biology, Max Planck Society, Max-Planck-Ring 5, 72076 Tübingen, DE, ou_3375790

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Abstract: Inference in matrix-variate Gaussian models has major applications for multioutput prediction and joint learning of row and column covariances from matrixvariate data. Here, we discuss an approach for efficient inference in such models that explicitly account for iid observation noise. Computational tractability can be retained by exploiting the Kronecker product between row and column covariance matrices. Using this framework, we show how to generalize the Graphical Lasso in order to learn a sparse inverse covariance between features while accounting for
a low-rank confounding covariance between samples. We show practical utility on applications to biology, where we model covariances with more than 100,000 dimensions.
We find greater accuracy in recovering biological network structures and are able to better reconstruct the confounders.

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Dates: Date issued: 2012-01

Publication Status: Issued

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Identifiers: BibTex Citekey: StegleLMLB2012

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Title: Twenty-Fifth Annual Conference on Neural Information Processing Systems (NIPS 2011)

Place of Event: Granada, Spain

Start-/End Date: 2011-12-12 - 2011-12-17

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Title: Advances in Neural Information Processing Systems 24

Source Genre: Proceedings

Creator(s):
Shawe-Taylor, J, Editor
Zemel, RS, Editor
Bartlett, P, Editor
Pereira, F, Editor
Weinberger, KQ, Editor

Affiliations:
-

Publ. Info: Red Hook, NY, USA : Curran

Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 630 - 638 Identifier: ISBN: 978-1-618-39599-3