Mind the Gap: Subspace based Hierarchical Domain Adaptation
This work addresses domain adaptation for machine learning applications where data has a hierarchical structure, representing an incremental improvement over existing subspace-based methods.
The paper tackled the problem of domain adaptation by considering hierarchical data organization and using multiple subspaces for source and target domains, resulting in consistent improvements over non-hierarchical baselines.
Domain adaptation techniques aim at adapting a classifier learnt on a source domain to work on the target domain. Exploiting the subspaces spanned by features of the source and target domains respectively is one approach that has been investigated towards solving this problem. These techniques normally assume the existence of a single subspace for the entire source / target domain. In this work, we consider the hierarchical organization of the data and consider multiple subspaces for the source and target domain based on the hierarchy. We evaluate different subspace based domain adaptation techniques under this setting and observe that using different subspaces based on the hierarchy yields consistent improvement over a non-hierarchical baseline