Nonparametric Bayesian feature selection for multi-task learning

We present a nonparametric Bayesian model for multi-task learning, with a focus on feature selection in binary classification. The model jointly identifies groups of similar tasks and selects the subset of features relevant to the tasks within each group. The model employs a Dirchlet process with a beta- Bernoulli hierarchical base measure. The posterior inference is accomplished efficiently using a Gibbs sampler. Experimental results are presented on simulated as well as real data.

[1]  T. Ferguson.  A Bayesian Analysis of Some Nonparametric Problems , 1973 .

[2]  Lawrence Carin,et al.  Multi-Task Learning for Classification with Dirichlet Process Priors , 2007, J. Mach. Learn. Res..

[3]  S. Chib,et al.  Bayesian analysis of binary and polychotomous response data , 1993 .

[4]  Rich Caruana,et al.  Multitask Learning , 1998, Encyclopedia of Machine Learning and Data Mining.

[5]  Jieping Ye,et al.  Multi-Task Feature Learning Via Efficient l2, 1-Norm Minimization , 2009, UAI.

[6]  Anil K. Jain,et al.  Bayesian learning of sparse classifiers , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[7]  Geoffrey E. Hinton,et al.  Learning and relearning in Boltzmann machines , 1986 .

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

[9]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[10]  Michael I. Jordan,et al.  Multi-task feature selection , 2006 .

[11]  Michael I. Jordan,et al.  Hierarchical Beta Processes and the Indian Buffet Process , 2007, AISTATS.