Manifold Regularized Multi-view Feature Selection for Web Image Annotation

  • Yangxi Li
  • Xin Shi
  • Lingling Tong
  • Yong Luo
  • Jinhui Tu
  • Xiaobo Zhu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8879)


The features used in many multimedia analysis-based applications are frequently of very high dimension. Feature selection offers several advantages in highly dimensional cases. Recently, multi-task feature selection has attracted much attention, and has been shown to often outperform the traditional single-task feature selection. Current multi-task feature selection methods are either supervised or unsupervised. In this paper, we address the semi-supervised multi-task feature selection problem. We first introduce manifold regularization in multi-task feature selection to utilize the limited number of labeled samples and the relatively large amount of unlabeled samples. However, the graph constructed in manifold regularization from a single feature representation (view) may be unreliable. We thus propose to construct the graph using the heterogeneous feature representations from multiple views. The proposed method is called manifold regularized multi-view feature selection (MRMVFS), which can exploit the label information, label relationship, data distribution, as well as correlation among different kinds of features simultaneously to boost the feature selection performance. All these information are integrated into a unified learning framework to estimate feature selection matrix, as well as the adaptive view weights. Experimental results on a real-world web image dataset demonstrate the effectiveness and superiority of the proposed MRMVFS over other state-of-the-art feature selection methods.


Feature selection multi-task multi-view manifold regularization 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Yangxi Li
    • 1
  • Xin Shi
    • 2
  • Lingling Tong
    • 1
  • Yong Luo
    • 2
  • Jinhui Tu
    • 3
  • Xiaobo Zhu
    • 1
  1. 1.National Computer Network Emergency Response Technical Team/Coordination Center of China (CNCERT/CC)China
  2. 2.Key Laboratory of Machine PerceptionPeking UniversityChina
  3. 3.China Construction BankGuangzhouChina

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