Abstract

Data heterogeneity is an intrinsic property of many high impact applications, such as insider threat detection, traffic prediction, brain image analysis, quality control in manufacturing processes, etc. Furthermore, multiple types of heterogeneity (e.g., task/view/instance heterogeneity) often co-exist in these applications, thus pose new challenges to existing techniques, most of which are tailored for a single or dual types of heterogeneity. To address this problem, in this paper, we propose a novel graph-based hybrid approach to simultaneously model multiple types of heterogeneity in a principled framework. The objective is to maximize the smoothness consistency of the neighboring nodes, bag-instance correlation together with task relatedness on the hybrid graphs, and simultaneously minimize the empirical classification loss. Furthermore, we analyze its performance based on Rademacher complexity, which sheds light on the benefits of jointly modeling multiple types of heterogeneity. To solve the resulting non-convex non-smooth problem, we propose an iterative algorithm named M3 Learning, which combines block coordinate descent and the bundle method for optimization. Experimental results on various data sets show the effectiveness of the proposed algorithm.

Original languageEnglish (US)
Title of host publicationProceedings - 15th IEEE International Conference on Data Mining, ICDM 2015
EditorsCharu Aggarwal, Zhi-Hua Zhou, Alexander Tuzhilin, Hui Xiong, Xindong Wu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1081-1086
Number of pages6
ISBN (Electronic)9781467395038
DOIs
StatePublished - Jan 5 2016
Event15th IEEE International Conference on Data Mining, ICDM 2015 - Atlantic City, United States
Duration: Nov 14 2015Nov 17 2015

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
Volume2016-January
ISSN (Print)1550-4786

Other

Other15th IEEE International Conference on Data Mining, ICDM 2015
CountryUnited States
CityAtlantic City
Period11/14/1511/17/15

Keywords

  • Heterogeneous learning
  • Multi-instance learning
  • Multi-task learning
  • Multi-view learning

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Yang, P., & He, J. (2016). A graph-based hybrid framework for modeling complex heterogeneity. In C. Aggarwal, Z-H. Zhou, A. Tuzhilin, H. Xiong, & X. Wu (Eds.), Proceedings - 15th IEEE International Conference on Data Mining, ICDM 2015 (pp. 1081-1086). [7373439] (Proceedings - IEEE International Conference on Data Mining, ICDM; Vol. 2016-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDM.2015.109