Sensor Selection in High-Dimensional Gaussian Trees with Nuisances
Name
How_sensor selection.pdf
Size
392.59 KB
Format
Adobe PDF
Checksum (MD5)
93fb94aa16c0e9f1f1dd271950459fc4
Author(s)
Levine, Daniel
How, Jonathan P.
Date Issued
2013
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
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.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
http://papers.nips.cc/book/advances-in-neural-information-processing-systems-26-2013
