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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 thedoi:10.1109/icdm.2014.42 dblp:conf/icdm/YangHYF14 fatcat:tjrxassbfvcsboro7wjj5zc4je