Learning from Label and Feature Heterogeneity

Pei Yang, Jingrui He, Hongxia Yang, Haoda Fu
2014 2014 IEEE International Conference on Data Mining  
Multiple types of heterogeneity, such as label heterogeneity and feature heterogeneity, often co-exist in many realworld data mining applications, such as news article categorization, gene functionality prediction. To effectively leverage such heterogeneity, in this paper, we propose a novel graphbased framework for Learning with both Label and Feature heterogeneities, namely L 2 F . It models the label correlation by requiring that any two label-specific classifiers behave similarly on the
more » ... views if the associated labels are similar, and imposes the view consistency by requiring that view-based classifiers generate similar predictions on the same examples. To solve the resulting optimization problem, we propose an iterative algorithm, which is guaranteed to converge to the global optimum. Furthermore, we analyze its generalization performance based on Rademacher complexity, which sheds light on the benefits of jointly modeling the label and feature heterogeneity. Experimental results on various data sets show the effectiveness of the proposed approach.
doi:10.1109/icdm.2014.42 dblp:conf/icdm/YangHYF14 fatcat:tjrxassbfvcsboro7wjj5zc4je