English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

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

MPS-Authors
/persons/resource/persons75313

Borgwardt,  Karsten       
Department Molecular Biology, Max Planck Institute for Developmental Biology, Max Planck Society;

Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

Stegle, O., Lippert, C., Mooij, J. M., Lawrence, N., & Borgwardt, K. (2011). Efficient inference in matrix-variate Gaussian models with iid observation noise. Advances in Neural Information Processing Systems 24 (NIPS 2011), 630-638.


Cite as: https://hdl.handle.net/21.11116/0000-000C-F375-E
Abstract
Inference in matrix-variate Gaussian models has major applications for multi- output prediction and joint learning of row and column covariances from matrix- variate 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 di- mensions. We find greater accuracy in recovering biological network structures and are able to better reconstruct the confounders.