Dual graph regularized NMF model for social event detection from Flickr data

Z. Yang, Qing Li, W. Liu, Y. Ma, M. Cheng

Research output: Journal article publicationJournal articleAcademic researchpeer-review

19 Citations (Scopus)


© 2016, Springer Science+Business Media New York.In this work, we aim to discover real-world events from Flickr data by devising a three-stage event detection framework. In the first stage, a multimodal fusion (MF) model is designed to deal with the heterogeneous feature modalities possessed by the user-shared data, which is advantageous in computation complexity. In the second stage, a dual graph regularized non-negative matrix factorization (DGNMF) model is proposed to learn compact feature representations. DGNMF incorporates Laplacian regularization terms for the data graph and base graph into the objective, keeping the geometry structures underlying the data samples and dictionary bases simultaneously. In the third stage, hybrid clustering algorithms are applied seamlessly to discover event clusters. Extensive experiments conducted on the real-world dataset reveal the MF-DGNMF-based approaches outperform the baselines.
Original languageEnglish
Pages (from-to)995-1015
Number of pages21
JournalWorld Wide Web
Issue number5
Publication statusPublished - 1 Sept 2017
Externally publishedYes


  • Data representation learning
  • Event detection
  • Multimedia content analysis
  • Multimodal fusion
  • Social media analytics

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications


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