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Learning to Classify from Impure Samples with High-Dimensional Data · Around

2018

Learning to Classify from Impure Samples with High-Dimensional Data

Komiske, Patrick T., Metodiev, Eric M., Nachman, Benjamin et al.

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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.

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