Heterogeneous Daily Living Activity Learning Through Domain Invariant Feature Subspace

Abstract

In the practical applications of supervised learning methods, the high cost of obtaining labeled data for learning tasks is a critical problem. One promising research area for solving the problem is transfer learning, which aims to learn a task in target domain by utilizing the training data in a different but related source domain. In this article, we propose a novel heterogeneous transfer learning algorithm called Heterogeneous Daily Living Activity Learning (HDLAL) which derives domain invariant feature representation space from cross-domain data distributions by projecting both domain data into the derived space in the close proximity of each other using Maximum Mean Discrepancy. Within the new feature space, we utilize ensemble classification algorithm to train multi-label classifier using the projected data to predict the labels in the target domain. We show the effectiveness of our approach by experimenting on real-world smart home datasets. The results shows that our HDLAL algorithm outperforms the common direct learning approaches in the context of predicting labels of activities of daily living (ADL).