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Matched-Pair Machine Learning

Summary: [This abstract is based on the authors' abstract.] Following an analogous distinction in statistical hypothesis testing and motivated by chemical plume detection in hyperspectral imagery, this article investigates machine-learning algorithms where the training set is composed of matched pairs. It is found that even conventional classifiers exhibit improved performance when the input data have a matched-pair structure, and an example of a “dipole” algorithm is developed to directly exploit this structured input. In some scenarios, matched pairs can be generated from independent samples, with the effect of not only doubling the nominal size of the training set, but of providing the matched-pair structure that leads to better learning. The creation of matched pairs from a dataset of interest also permits a kind of transductive learning, which is found for the plume detection problem to exhibit improved performance. Supplementary materials for this article are available online.

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  • Topics: Statistics
  • Keywords: Algorithm, Classification, Spectrometry, Hypothesis testing, Signal
  • Author: Theiler, James;
  • Journal: Technometrics