Approximately Optimal Domain Adaptation with Fisher’s Linear Discriminant

Autor:	Hayden Helm, Ashwin de Silva, Joshua T. Vogelstein, Carey E. Priebe, Weiwei Yang
Jazyk:	angličtina
Rok vydání:	2024
Předmět:	statistical learning domain adaptation transfer learning physiological prediction linear classifiers Mathematics QA1-939
Zdroj:	Mathematics, Vol 12, Iss 5, p 746 (2024)
Druh dokumentu:	article
ISSN:	2227-7390
DOI:	10.3390/math12050746
Popis:	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.
Databáze:	Directory of Open Access Journals
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