Multi-Source Iterative Adaptation for Cross-Domain Classification

Abstract

Owing to the tremendous increase in the volume and variety of user generated content, train-once-apply-forever models are insufficient for supervised learning tasks. Thus, developing algorithms that adapt across domains by leveraging data from multiple domains is critical. However, existing adaptation algorithms often fail to identify the right sources to use for adaptation. In this work, we present a novel multi-source iterative domain adaptation algorithm (MSIDA) that leverages knowledge from selective sources to improve the performance in a target domain. The algorithm first chooses the best K sources from possibly numerous existing domains taking into account both similarity and complementarity properties of the domains. Then it learns target specific features in an iterative manner building on the common shared representations from the source domains. We give theoretical justifications for our source selection procedure and also give mistake bounds for the MSIDA algorithm. Experimental results justify the theory as MSIDA significantly outperforms existing cross-domain classification approaches on the real world and benchmark datasets.