Sparse Nonparametric Topic Model for Transfer Learning
Ali Faisal, Jussi Gillberg, Jaakko Peltonen, Gayle Leen, Samuel Kaski · 2012
Abstract. Count data arises for example in bioinformatics or analysis of text documents represented as word count vectors. With several data sets available from related sources, exploiting their similarities by transfer learning can improve models compared to modeling sources independently. We introduce a Bayesian generative transfer learning model which represents similarity across document collections by sparse sharing of latent topics controlled by an Indian Buffet Process. Unlike Hierarchical Dirichlet Process based multi-task learning, our model decouples topic sharing probability from topic strength, making sharing of low-strength topics easier, and outperforms the HDP approach in experiments. 1