Heterogeneous Daily Living Activity Learning Through Domain Invariant Feature Subspace

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 learni...