2013 IEEE 13th International Conference on Data Mining (2013)
Dallas, TX, USA USA
Dec. 7, 2013 to Dec. 10, 2013
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2013.70
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 Generalized Inverse Gaussian (MGIG) distribution and 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. 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 Cora dataset demonstrate the promise of the proposed algorithms.
Covariance matrices, Bayes methods, Algorithm design and analysis, Optimization, Machine learning algorithms, Gaussian distribution, Kernel
Y. Li, M. Yang, Z. Qi and Z. M. Zhang, "Bayesian Multi-Task Relationship Learning with Link Structure," 2013 IEEE 13th International Conference on Data Mining(ICDM), Dallas, TX, USA USA, 2013, pp. 1115-1120.