Sparse Bayesian Multi-Task Learning
Cedric Archambeau, Shengbo Guo, Onno Zoeter
We propose a new sparse Bayesian model for multi-task regression and classification.
The model is able to capture correlations between tasks, or more specifically
a low-rank approximation of the covariance matrix, while being sparse in the features.
We introduce a general family of group sparsity inducing priors based on
matrix-variate Gaussian scale mixtures. We show the amount of sparsity can be
learnt from the data by combining an approximate inference approach with type
II maximum likelihood estimation of the hyperparameters. Empirical evaluations
on data sets from biology and vision demonstrate the applicability of the model,
where on both regression and classification tasks it achieves competitive predictive performance compared to previously proposed methods.
Neural Information Processing Systems Foundation (NIPS), Granada, Spain, December 12-17, 2011.