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Sensor Selection in High-Dimensional Gaussian Trees with Nuisances

Author(s)
Levine, Daniel; How, Jonathan P.
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Abstract
We consider the sensor selection problem on multivariate Gaussian distributions where only a \emph{subset} of latent variables is of inferential interest. For pairs of vertices connected by a unique path in the graph, we show that there exist decompositions of nonlocal mutual information into local information measures that can be computed efficiently from the output of message passing algorithms. We integrate these decompositions into a computationally efficient greedy selector where the computational expense of quantification can be distributed across nodes in the network. Experimental results demonstrate the comparative efficiency of our algorithms for sensor selection in high-dimensional distributions. We additionally derive an online-computable performance bound based on augmentations of the relevant latent variable set that, when such a valid augmentation exists, is applicable for \emph{any} distribution with nuisances.
Date issued
2013
URI
http://hdl.handle.net/1721.1/96962
Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics; Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Journal
Advances in Neural Information Processing Systems (NIPS) 26
Publisher
Neural Information Processing Systems Foundation
Citation
Levine, Daniel, and Jonathan P. How. "Sensor Selection in High-Dimensional Gaussian Trees with Nuisances." Advances in Neural Information Processing Systems (NIPS) 26, 2013.
Version: Final published version
ISSN
1049-5258

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