Bayesian Multi Task Relationship Learning with Link Structure
In this paper, we study the multi task learning problem with a new perspective of considering the link structure of data and task relationship modeling simultaneously. In particular, we first introduce the Matrix Gaussian (MG) distribution and Matrix Generalized Inverse Gaussian (MGIG) distribution, then define a Matrix Gaussian Matrix Generalized Inverse Gaussian (MG MGIG) prior. Based on this prior, we propose a novel multi task learning algorithm, the Bayesian Multi task Relationship Learning (BMTRL) algorithm. To incorporate the link structure into the framework of BMTRL, we propose link constraints between samples. Through combining the BMTRL algorithm with the link constraints, we propose the Bayesian Multi task Relationship Learning with Link Constraints (BMTRL LC) algorithm. Further, we apply the manifold theory to provide an extension of BMTRL LC to data with no link structure. Specifically, BMTRL LC is effective for multi task learning with only limited training samples, which is not addressed in the existing literature. To make the computation tractable, we simultaneously use a convex optimization method and sampling techniques. In particular, we adopt two stochastic EM algorithms for BMTRL and BMTRL LC, respectively. The experimental results on three real datasets demonstrate the promise of the proposed algorithms.
IEEE Trans. Knowl. Data Eng.