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Near-optimal sensor placements in Gaussian processes
Open accessAbstract
When monitoring spatial phenomena, which are often modeled as Gaussian Processes (GPs), choosing sensor locations is a fundamental task. A common strategy is to place sensors at the points of highest entropy (variance) in the GP model. We propose a mutual information criteria, and show that it produces better placements. Furthermore, we prove that finding the configuration that maximizes mutual information is NP-complete. To address this issue, we describe a polynomial-time approximation that is within (1 -- 1/e) of the optimum by exploiting the submodularity of our criterion. This algorithm is extended to handle local structure in the GP, yielding significant speedups. We demonstrate the advantages of our approach on two real-world data sets.
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Golub, G. (2012) · doi:10.56021/9781421407944 · Cited by 34,505
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Bayesian Active Learning for Classification and Preference Learning
Houlsby, N., Huszár, F., Ghahramani, Z., & Lengyel, M. (2011) · arXiv · doi:10.48550/arxiv.1112.5745 · Cited by 507
“[Guestrin et al., 2005, Krause et al., 2006] also use mutual information.”
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Romero, P. A., Krause, A., & Arnold, F. H. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1215251110 · Cited by 360
Self-citationBayesian active probabilistic classification for psychometric field estimation
Song, X. D., Sukesan, K. A., & Barbour, D. L. (2018) · Attention, Perception, & Psychophysics · doi:10.3758/s13414-017-1460-0 · Cited by 22
Metadata
- Authors
- Guestrin, C., Krause, A., & Singh, A. P.
- Venue
- Proceedings of the 22nd international conference on Machine learning - ICML '05
- Publisher
- ACM Press
- Published
- 2005
- Pages
- 265-272
- OpenAlex
- W1999824796
- Cited by
- 454
- References
- 1
- FWCI
- 13.36
- Type
- conference paper
- Language
- en
- Open access
- green
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- Guestrin, C., Krause, A., & Singh, A. P. (2005). Near-optimal sensor placements in Gaussian processes. Proceedings of the 22nd international conference on Machine learning - ICML '05. https://doi.org/10.1145/1102351.1102385
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