Improving Self-Adaptation For Multi-Sensor Activity Recognition with Active Learning

Heterogeneous domain adaptation adapts a machine learning model, here classification model, from a source domain to a target domain to leverage data from both domains. Thereby, supervised heterogeneous domain adaptation expects labeled data from the target domain, while unsupervised heterogeneous domain adaptation does not. In this article, we study the inclusion of active learning to bridge unsupervised and supervised domain adaptation. The active learning approach iteratively queries the most useful instances from the target domain, which are then labeled and used to improve the classification model. Using active learning, the selection of training instances can focus on areas where ambiguity in the source domain resolves in the target domain. Hence, we achieve the same performance with fewer labels. Experiments on real activity recognition data confirm our claims.