Approximately optimal domain adaptation with Fisher's Linear Discriminant
arxiv(2023)
摘要
We propose a class of models based on Fisher's Linear Discriminant (FLD) in
the context of domain adaptation. The class is the convex combination of two
hypotheses: i) an average hypothesis representing previously seen source tasks
and ii) a hypothesis trained on a new target task. For a particular generative
setting we derive the optimal convex combination of the two models under 0-1
loss, propose a computable approximation, and study the effect of various
parameter settings on the relative risks between the optimal hypothesis,
hypothesis i), and hypothesis ii). We demonstrate the effectiveness of the
proposed optimal classifier in the context of EEG- and ECG-based classification
settings and argue that the optimal classifier can be computed without access
to direct information from any of the individual source tasks. We conclude by
discussing further applications, limitations, and possible future directions.
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关键词
optimal domain adaptation,fisher
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