Abstract

In many real world applications such as satellite image analysis, gene function prediction, and insider threat detection, the data collected from heterogeneous sources often exhibit multiple types of heterogeneity, such as task heterogeneity, view heterogeneity, and label heterogeneity. To address this problem, we propose a Hierarchical Multi-Latent Space (HiMLS) learning approach to jointly model the triple types of heterogeneity. The basic idea is to learn a hierarchical multi-latent space by which we can simultaneously leverage the task relatedness, view consistency and the label correlations to improve the learning performance. We first propose a multi-latent space framework to model the complex heterogeneity, which is used as a building block to stack up a multi-layer structure so as to learn the hierarchical multilatent space. In such a way, we can gradually learn the more abstract concepts in the higher level. Then, a deep learning algorithm is proposed to solve the optimization problem. The experimental results on various data sets show the effectiveness of the proposed approach.

Original languageEnglish (US)
Title of host publicationProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages1375-1384
Number of pages10
Volume2015-August
ISBN (Print)9781450336642
DOIs
StatePublished - Aug 10 2015
Event21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2015 - Sydney, Australia
Duration: Aug 10 2015Aug 13 2015

Other

Other21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2015
CountryAustralia
CitySydney
Period8/10/158/13/15

Keywords

  • Heterogeneous learning
  • Multi-label learning
  • Multi-task learning
  • Multi-view learning

ASJC Scopus subject areas

  • Software
  • Information Systems

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  • Cite this

    Yang, P., & He, J. (2015). Model multiple heterogeneity via hierarchical multi-latent space learning. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Vol. 2015-August, pp. 1375-1384). Association for Computing Machinery. https://doi.org/10.1145/2783258.2783330