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.

[1]  Michael L. Littman,et al.  Activity Recognition from Accelerometer Data , 2005, AAAI.

[2]  Xu Sun,et al.  Averaged Stochastic Gradient Descent with Feedback: An Accurate, Robust, and Fast Training Method , 2010, 2010 IEEE International Conference on Data Mining.

[3]  Michael R. Lyu,et al.  Online learning for multi-task feature selection , 2010, CIKM '10.

[4]  Michael R. Lyu,et al.  Learning for MultiTask Feature Selection , 2010 .

[5]  David B. Dunson,et al.  The matrix stick-breaking process for flexible multi-task learning , 2007, ICML '07.

[6]  Pierre Priouret,et al.  Adaptive Algorithms and Stochastic Approximations , 1990, Applications of Mathematics.

[7]  Massimiliano Pontil,et al.  Convex multi-task feature learning , 2008, Machine Learning.

[8]  Bernt Schiele,et al.  Discovery of activity patterns using topic models , 2008 .

[9]  Ling Bao,et al.  Activity Recognition from User-Annotated Acceleration Data , 2004, Pervasive.

[10]  Peter L. Bartlett,et al.  Matrix regularization techniques for online multitask learning , 2008 .

[11]  Andrew McCallum,et al.  Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.

[12]  Xu Sun,et al.  Large Scale Real-Life Action Recognition Using Conditional Random Fields with Stochastic Training , 2011, PAKDD.

[13]  Johannes Peltola,et al.  Activity classification using realistic data from wearable sensors , 2006, IEEE Transactions on Information Technology in Biomedicine.