Learning to classify from impure samples with high-dimensional data
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PhysRevD.98.011502.pdf
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334.23 KB
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Author(s)
Nachman, Benjamin
Schwartz, Matthew D.
Komiske, Patrick T.
Metodiev, Eric Mario
Date Issued
July 2018
Journal
Physical Review D
Publisher
American Physical Society
Citation
Komiske, Patrick T., Eric M. Metodiev, Benjamin Nachman, and Matthew D. Schwartz. “Learning to Classify from Impure Samples with High-Dimensional Data.” Physical Review D 98, no. 1 (July 16, 2018).
Version
Final published version
Abstract
A persistent challenge in practical classification tasks is that labeled training sets are not always available. In particle physics, this challenge is surmounted by the use of simulations. These simulations accurately reproduce most features of data, but cannot be trusted to capture all of the complex correlations exploitable by modern machine learning methods. Recent work in weakly supervised learning has shown that simple, low-dimensional classifiers can be trained using only the impure mixtures present in data. Here, we demonstrate that complex, high-dimensional classifiers can also be trained on impure mixtures using weak supervision techniques, with performance comparable to what could be achieved with pure samples. Using weak supervision will therefore allow us to avoid relying exclusively on simulations for high-dimensional classification. This work opens the door to a new regime whereby complex models are trained directly on data, providing direct access to probe the underlying physics.
MIT Department
Massachusetts Institute of Technology. Center for Theoretical Physics
Massachusetts Institute of Technology. Department of Physics
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1103/PhysRevD.98.011502
