Approximately optimal domain adaptation with Fisher's Linear Discriminant

arXiv:2302.14186v3 Announce Type: replace-cross
Abstract: 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 …

abstract arxiv class combination context cs.lg domain domain adaptation eess.sp fisher hypothesis linear stat.ap stat.me stat.ml tasks type