Adaptive structure discovery for multimedia analysis using multiple features

Kun Zhan, Xiaojun Chang, Junpeng Guan, Ling Chen, Zhigang Ma, Yi Yang

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12 Citations (Scopus)


Multifeature learning has been a fundamental research problem in multimedia analysis. Most existing multifeature learning methods exploit graph, which must be computed beforehand, as input to uncover data distribution. These methods have two major problems confronted. First, graph construction requires calculating similarity based on nearby data pairs by a fixed function, e.g., the RBF kernel, but the intrinsic correlation among different data pairs varies constantly. Therefore, feature learning based on such predefined graphs may degrade, especially when there is dramatic correlation variation between nearby data pairs. Second, in most existing algorithms, each single-feature graph is computed independently and then combine them for learning, which ignores the correlation between multiple features. In this paper, a new unsupervised multifeature learning method is proposed to make the best utilization of the correlation among different features by jointly optimizing data correlation from multiple features in an adaptive way. As opposed to computing the affinity weight of data pairs by a fixed function, the weight of affinity graph is learned by a well-designed optimization problem. Additionally, the affinity graph of data pairs from different features is optimized in a global level to better leverage the correlation among different channels. In this way, the adaptive approach correlates the features of all features for a better learning process. Experimental results on real-world datasets demonstrate that our approach outperforms the state-of-the-art algorithms on leveraging multiple features for multimedia analysis.

Original languageEnglish
Pages (from-to)1826-1834
Number of pages9
JournalIEEE Transactions on Cybernetics
Issue number5
Publication statusPublished - May 2019
Externally publishedYes


  • Clustering algorithms
  • Correlation
  • Dimensionality reduction
  • Graph learning
  • Laplace equations
  • Learning systems
  • Linear programming
  • multimedia analysis
  • Multimedia communication
  • multiview clustering
  • unsupervised learning

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