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Large-margin multi-view Gaussian process

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Abstract

In image classification, the goal was to decide whether an image belongs to a certain category or not. Multiple features are usually employed to comprehend the contents of images substantially for the improvement of classification accuracy. However, it also brings in some new problems that how to effectively combine multiple features together and how to handle the high-dimensional features from multiple views given the small training set. In this paper, we integrate the large-margin idea into the Gaussian process to discover the latent subspace shared by multiple features. Therefore, our approach inherits all the advantages of Gaussian process and large-margin principle. A probabilistic explanation is provided by Gaussian process to embed multiple features into the shared low-dimensional subspace, which derives a strong discriminative ability from the large-margin principle, and thus, the subsequent classification task can be effectively accomplished. Finally, we demonstrate the advantages of the proposed algorithm on real-world image datasets for discovering discriminative latent subspace and improving the classification performance.

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Acknowledgments

The work was supported in part by ARC FT130101457, NBRPC 2011CB302400, NSFC 61121002, 61375026, and JCYJ 20120614152136201.

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Authors and Affiliations

  1. Key Laboratory of Machine Perception (Ministry of Education), Peking University, Beijing, China

    Chang Xu & Chao Xu

  2. Centre for Quantum Computation & Intelligent Systems, Faculty of Engineering and Information Technology, University of Technology, Sydney, 235 Jones Street, Ultimo, NSW, 2007, Australia

    Dacheng Tao

  3. National Computer Network Emergency Response Technical Team/Coordination Center of China (CNCERT/CC), Beijing, China

    Yangxi Li

Authors
  1. Chang Xu
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  2. Dacheng Tao
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  3. Yangxi Li
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  4. Chao Xu
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Correspondence to Dacheng Tao.

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Xu, C., Tao, D., Li, Y. et al. Large-margin multi-view Gaussian process. Multimedia Systems 21, 147–157 (2015). https://doi.org/10.1007/s00530-014-0389-6

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