Adaptive Unsupervised Multi-view Feature Selection for Visual Concept Recognition

  • Yinfu Feng
  • Jun Xiao
  • Yueting Zhuang
  • Xiaoming Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7724)


To reveal and leverage the correlated and complemental information between different views, a great amount of multi-view learning algorithms have been proposed in recent years. However, unsupervised feature selection in multi-view learning is still a challenge due to lack of data labels that could be utilized to select the discriminative features. Moreover, most of the traditional feature selection methods are developed for the single-view data, and are not directly applicable to the multi-view data. Therefore, we propose an unsupervised learning method called Adaptive Unsupervised Multi-view Feature Selection (AUMFS) in this paper. AUMFS attempts to jointly utilize three kinds of vital information, i.e., data cluster structure, data similarity and the correlations between different views, contained in the original data together for feature selection. To achieve this goal, a robust sparse regression model with the l 2,1-norm penalty is introduced to predict data cluster labels, and at the same time, multiple view-dependent visual similar graphs are constructed to flexibly model the visual similarity in each view. Then, AUMFS integrates data cluster labels prediction and adaptive multi-view visual similar graph learning into a unified framework. To solve the objective function of AUMFS, a simple yet efficient iterative method is proposed. We apply AUMFS to three visual concept recognition applications (i.e., social image concept recognition, object recognition and video-based human action recognition) on four benchmark datasets. Experimental results show the proposed method significantly outperforms several state-of-the-art feature selection methods. More importantly, our method is not very sensitive to the parameters and the optimization method converges very fast.


Feature Selection Feature Selection Method Nonnegative Matrix Factorization Concept Recognition Holistic Feature 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yinfu Feng
    • 1
  • Jun Xiao
    • 1
  • Yueting Zhuang
    • 1
  • Xiaoming Liu
    • 2
  1. 1.School of Computer ScienceZhejiang UniversityHangzhouP.R.China
  2. 2.Department of Computer Science and EngineeringMichigan State UniversityUSA

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