Approximately optimal domain adaptation with Fisher's Linear Discriminant
This addresses domain adaptation for EEG and ECG classification, but it is incremental as it builds on existing FLD methods.
The paper tackles domain adaptation by proposing a convex combination of source and target hypotheses using Fisher's Linear Discriminant, deriving an optimal classifier under 0-1 loss and demonstrating effectiveness in EEG- and ECG-based classification settings.
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.