Abstract

Traditional data mining techniques are designed to model a single type of heterogeneity, such as multi-task learning for modeling task heterogeneity, multi-view learning for modeling view heterogeneity, etc. Recently, a variety of real applications emerged, which exhibit dual heterogeneity, namely both task heterogeneity and view heterogeneity. Examples include insider threat detection across multiple organizations, web image classification in different domains, etc. Existing methods for addressing such problems typically assume that multiple tasks are equally related and multiple views are equally consistent, which limits their application in complex settings with varying task relatedness and view consistency. In this paper, we advance state-of-the-art techniques by adaptively modeling task relatedness and view consistency via a nonparametric Bayes model: we model task relatedness using normal penalty with sparse covariances, and view consistency using matrix Dirichlet process. Based on this model, we propose the NOBLE algorithm using an efficient Gibbs sampler. Experimental results on multiple real data sets demonstrate the effectiveness of the proposed algorithm.

Original languageEnglish (US)
Title of host publicationKDD 2014 - Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages582-590
Number of pages9
ISBN (Print)9781450329569
DOIs
StatePublished - Jan 1 2014
Event20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2014 - New York, NY, United States
Duration: Aug 24 2014Aug 27 2014

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2014
CountryUnited States
CityNew York, NY
Period8/24/148/27/14

Keywords

  • Gibbs sampler
  • multi-task multi-view
  • nonparametric Bayes modeling

ASJC Scopus subject areas

  • Software
  • Information Systems

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  • Cite this

    Yang, H., & He, J. (2014). Learning with dual heterogeneity: A nonparametric Bayes model. In KDD 2014 - Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 582-590). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). Association for Computing Machinery. https://doi.org/10.1145/2623330.2623727