Springer Berlin Heidelberg
Abstract Multi-task learning involves solving multiple related learning problems by sharing some common structure for improved generalization performance. A promising idea to multi- task learning is joint feature selection where a sparsity pattern is shared across task specific feature representations. In this paper, we propose a novel Gaussian Process (GP) approach to multi-task learning based on joint feature selection. The novelty of the proposed approach is that it captures the task similarity by sharing a sparsity pattern over the kernel hyper- ...