Automatic Domain Partitioning for Multi-Domain Learning

Di Wang, Chenyan Xiong, William Yang Wang

Abstract: Multi-Domain learning (MDL) assumes that the domain labels in the dataset are known. However, when there are multiple metadata attributes available, it is not always straightforward to select a single best attribute for domain partition, and it is possible that combining more than one metadata attributes (including continuous attributes) can lead to better MDL performance. In this work, we propose an automatic domain partitioning approach that aims at providing better domain identities for MDL. We use a supervised clustering approach that learns the domain distance between data instances , and then cluster the data into better domains for MDL. Our experiment on real multi-domain datasets shows that using our automatically generated domain partition improves over popular MDL methods.