Unsupervised Multi-view Feature Extraction with Dynamic Graph Learning

Dan Shi, Lei Zhu, Zhiyong Cheng, Zhihui Li, Huaxiang Zhang

Research output: Contribution to journalArticleResearchpeer-review

31 Citations (Scopus)


Graph-based multi-view feature extraction has attracted much attention in literature. However, conventional solutions generally rely on a manually defined affinity graph matrix, which is hard to capture the intrinsic sample relations in multiple views. In addition, the graph construction and feature extraction are separated into two independent processes which may result in sub-optimal results. Furthermore, the raw data may contain adverse noises that reduces the reliability of the affinity matrix. In this paper, we propose a novel Unsupervised Multi-view Feature Extraction with Dynamic Graph Learning (UMFE-DGL) to solve these limitations. We devise a unified learning framework which simultaneously performs dynamic graph learning and the feature extraction. Dynamic graph learning adaptively captures the intrinsic multiple view-specific relations of samples. Feature extraction learns the projection matrix that could accordingly preserve the dynamically adjusted sample relations modelled by graph into the low-dimensional features. Experimental results on several public datasets demonstrate the superior performance of the proposed approach, compared with state-of-the-art techniques.

Original languageEnglish
Pages (from-to)256-264
Number of pages9
JournalJournal of Visual Communication and Image Representation
Publication statusPublished - Oct 2018
Externally publishedYes


  • Dynamic graph learning
  • Intrinsic sample relations
  • Multi-view feature extraction

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