Approximately Optimal Domain Adaptation with Fisher’s Linear Discriminant

Hayden Helm (), Ashwin de Silva, Joshua T. Vogelstein, Carey E. Priebe and Weiwei Yang
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Hayden Helm: Microsoft Research, Redmond, WA 98052, USA
Ashwin de Silva: Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
Joshua T. Vogelstein: Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
Carey E. Priebe: Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD 21218, USA
Weiwei Yang: Microsoft Research, Redmond, WA 98052, USA

Mathematics, 2024, vol. 12, issue 5, 1-20

Abstract: We propose and study a data-driven method that can interpolate between a classical and a modern approach to classification for a class of linear models. The class is the convex combinations of an average of the source task classifiers and a classifier trained on the limited data available for the target task. We derive the expected loss of an element in the class with respect to the target distribution for a specific generative model, propose a computable approximation of the loss, and demonstrate that the element of the proposed class that minimizes the approximated risk is able to exploit a natural bias–variance trade-off in task space in both simulated and real-data settings. We conclude by discussing further applications, limitations, and potential future research directions.

Keywords: statistical learning; domain adaptation; transfer learning; physiological prediction; linear classifiers (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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