Learn-on-the-go: Autonomous cross-subject context learning for internet-of-things applications

Developing machine learning algorithms for applications of Internet-of-Things requires collecting a large amount of labeled training data, which is an expensive and labor-intensive process. Upon a minor change in the context, for example utilization by a new user, the model will need re-training to maintain the initial performance. To address this problem, we propose a graph model and an unsupervised label transfer algorithm (learn-on-the-go) which exploits the relations between source and target user data to develop a highly-accurate and scalable machine learning model. Our analysis on real-world data demonstrates 54% and 22% performance improvement against baseline and state-of-the-art solutions, respectively.