Improved linear regression prediction by transfer learning

David Obst, Badih Ghattas, Sandra Claudel, Jairo Cugliari, Yannig Goude and Georges Oppenheim

Computational Statistics & Data Analysis, 2022, vol. 174, issue C

Abstract: Transfer learning, also referred as knowledge transfer, aims at reusing knowledge from a source dataset to a similar target one. While several studies address the problem of what to transfer, the very important question of when to answer remains mostly unanswered, especially from a theoretical point-of-view for regression problems. A new theoretical framework for the problem of parameter transfer for the linear model is proposed. It is shown that the quality of transfer for a new input vector depends on its representation in an eigenbasis involving the parameters of the problem. Furthermore, a statistical test is constructed to predict whether a fine-tuned model has a lower prediction quadratic risk than the base target model for an unobserved sample. Efficiency of the test is illustrated on synthetic data as well as real electricity consumption data.

Keywords: Linear regression; Transfer learning; Statistical test; Fine-tuning; Transfer theory (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:174:y:2022:i:c:s0167947322000792

DOI: 10.1016/j.csda.2022.107499

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