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Sensor Selection in High-Dimensional Gaussian Trees with Nuisances
Daniel S Levine · Jonathan How

Thu Dec 05 07:00 PM -- 11:59 PM (PST) @ Harrah's Special Events Center, 2nd Floor #None

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.

Author Information

Daniel S Levine (Massachusetts Institute of Technology)
Jonathan How (MIT)

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