Multi-view Intact Discriminant Space Learning for Image Classification

Abstract

Different views of one object usually represent different aspects of the object, and a single view is unlikely to comprehensively describe the object. In multi-view learning, comprehensive utilization of multi-view information is helpful. In this paper, we propose a novel supervised latent subspace learning method called multi-view intact discriminant space learning (MIDSL) by efficiently integrating complementary multi-view information of different views. MIDSL learns a latent intact discriminant space by employing Fisher discrimination criterion to fully use class label information, which can well guide exploiting useful discriminant information, of labeled training samples. MIDSL can simultaneously minimize the within-class scatter and maximize the between-class scatter of the feature representations of different objects in the learned latent intact discriminant space. Aiming to utilize unlabeled samples to help mining more useful information for better learning latent intact discriminant space, we extend MIDSL method in semi-supervised scenario and propose semi-supervised multi-view intact discriminant space learning (SMIDSL) method. We further extend MIDSL and SMIDSL methods by kernel technique and propose kernelized multi-view intact discriminant space learning (KMIDSL) and kernelized semi-supervised multi-view intact discriminant space learning (KSMIDSL) methods. Experimental results on Caltech 101, LFW, MNIST and RGB-D datasets demonstrate the effectiveness of our proposed methods.

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Acknowledgements

This work is partially funded by the National Science Foundation of China (NSFC) under Grant Numbers of 61272273 and 61702280. It is also supported by NSFC-Key Project of General Technology Fundamental Research United Fund (No. U1736211), Natural Science Foundation of Jiangsu Province (No. BK20170900), Natural Science Fund for Colleges and Universities in Jiangsu Province (No. 17KJB520025), the Scientific Research Staring Foundation for Introduced Talents in NJUPT (NUPTSF, No. NY217009), Nanjing University of Posts and Telecommunications (No. XJKY14016). In addition, it is also funded by Education Department of Jiangxi Province under Grant No. GJJ151076.

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Correspondence to Xiao-Yuan Jing.

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Dong, X., Wu, F., Jing, X. et al. Multi-view Intact Discriminant Space Learning for Image Classification. Neural Process Lett 50, 1661–1685 (2019). https://doi.org/10.1007/s11063-018-9951-0

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Keywords

  • Multi-view learning
  • Subspace learning
  • Fisher discrimination criterion
  • Image classification
  • Semi-supervised multi-view learning