A graph-based framework for multi-task multi-view learning

Jingrui He, Rick Lawrence

Research output: Chapter in Book/Report/Conference proceedingConference contribution

97 Scopus citations

Abstract

Many real-world problems exhibit dual-heterogeneity. A single learning task might have features in multiple views (i.e., feature heterogeneity); multiple learning tasks might be related with each other through one or more shared views (i.e., task heterogeneity). Existing multi-task learning or multi-view learning algorithms only capture one type of heterogeneity. In this paper, we introduce Multi-Task MultiView (M2TV) learning for such complicated learning problems with both feature heterogeneity and task heterogeneity. We propose a graph-based framework (GraM2) to take full advantage of the dual-heterogeneous nature. Our framework has a natural connection to Reproducing Kernel Hilbert Space (RKHS). Furthermore, we propose an iterative algorithm (IteM2) for GraM2 framework, and analyze its optimality, convergence and time complexity. Experimental results on various real data sets demonstrate its effectiveness.

Original languageEnglish (US)
Title of host publicationProceedings of the 28th International Conference on Machine Learning, ICML 2011
Pages25-32
Number of pages8
StatePublished - Oct 7 2011
Event28th International Conference on Machine Learning, ICML 2011 - Bellevue, WA, United States
Duration: Jun 28 2011Jul 2 2011

Publication series

NameProceedings of the 28th International Conference on Machine Learning, ICML 2011

Other

Other28th International Conference on Machine Learning, ICML 2011
CountryUnited States
CityBellevue, WA
Period6/28/117/2/11

ASJC Scopus subject areas

  • Computer Science Applications
  • Human-Computer Interaction
  • Education

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