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

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

2 Scopus Citations
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Original languageEnglish
Pages (from-to)995-1015
Journal / PublicationWorld Wide Web
Issue number5
Early online date28 Oct 2016
StatePublished - Sep 2017


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.

Research Area(s)

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

Citation Format(s)