Online Bayesian Transfer Learning for Sequential Data Modeling

Priyank Jaini, Zhitang Chen, Pablo Carbajal, Edith Law, Laura Middleton, Kayla Regan, Mike Schaekermann, George Trimponias, James Tung, Pascal Poupart

Abstract: We consider the problem of inferring a sequence of hidden states associated with a sequence of observations produced by an individual within a population. Instead of learning a single sequence model for the population (which does not account for variations within the population), we learn a set of basis sequence models based on different individuals. The sequence of hidden states for a new individual is inferred in an online fashion by estimating a distribution over the basis models that best explain the sequence of observations of this new individual. We explain how to do this in the context of hidden Markov models with Gaussian mixture models that are learned based on streaming data by online Bayesian moment matching. The resulting transfer learning technique is demonstrated with three real-word applications: activity recognition based on smartphone sensors, sleep classification based on electroencephalography data and the prediction of the direction of future packet flows between a pair of servers in telecommunication networks.

Conflicts: uwaterloo.ca

Keywords: Unsupervised Learning, Transfer Learning, Applications