In many real world applications such as image annotation, 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 framework 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 approach to model the complex heterogeneity, which is then used as a building block to stack up a multi-layer structure in order to learn the hierarchical multi-latent space. In such a way, we can gradually learn the more abstract concepts in the higher level. We present two instantiated models of the generalized framework using different divergence measures. The two-phase learning algorithms are used to train the multi-layer models. We drive the multiplicative update rules for pre-training and fine-tuning in each model, and prove the convergence and correctness of the update methods. The effectiveness of the proposed approach is verified on various data sets.

Original languageEnglish (US)
Article number7572203
Pages (from-to)3154-3168
Number of pages15
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number12
StatePublished - Dec 1 2016


  • Heterogeneous learning
  • matrix tri-factorization
  • multi-label learning
  • multi-task learning
  • multi-view learning

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics


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