Theoretical guarantees on penalized information gathering

Abstract

Optimal measurement selection for inference is combinatorially complex and intractable for large scale problems. Under mild technical conditions, it has been proven that greedy heuristics combined with conditional mutual information rewards achieve performance within a factor of the optimal. Here we provide conditions under which cost-penalized mutual information may achieve similar guarantees. Specifically, if the cost of a measurement is proportional to the information it conveys, the bounds proven in [4] and [10] still apply.

DOI: 10.1109/SSP.2012.6319688

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Cite this paper

@article{Papachristoudis2012TheoreticalGO, title={Theoretical guarantees on penalized information gathering}, author={Georgios Papachristoudis and John W. Fisher}, journal={2012 IEEE Statistical Signal Processing Workshop (SSP)}, year={2012}, pages={301-304} }