A New Multi-task Learning Method for Personalized Activity Recognition

Personalized activity recognition usually faces the problem of data sparseness. We aim at improving accuracy of personalized activity recognition by incorporating the information from other persons. We propose a new online multi-task learning method for personalized activity recognition. The proposed online multi-task learning method automatically learns the ``transfer-factors" (similarities) among different tasks (i.e., among different persons in our case). Experiments demonstrate that the proposed method significantly outperforms existing methods. The novelty of this paper is twofold: (1) A new multi-task learning framework, which can naturally learn similarities among tasks, (2) To our knowledge, this is the first study of large-scale personalized activity recognition.