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Multi-view Discriminative Learning via Joint Non-negative Matrix Factorization

  • Zhong Zhang
  • Zhili Qin
  • Peiyan Li
  • Qinli Yang
  • Junming ShaoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10828)

Abstract

Multi-view learning attempts to generate a classifier with a better performance by exploiting relationship among multiple views. Existing approaches often focus on learning the consistency and/or complementarity among different views. However, not all consistent or complementary information is useful for learning, instead, only class-specific discriminative information is essential. In this paper, we propose a new robust multi-view learning algorithm, called DICS, by exploring the Discriminative and non-discriminative Information existing in Common and view-Specific parts among different views via joint non-negative matrix factorization. The basic idea is to learn a latent common subspace and view-specific subspaces, and more importantly, discriminative and non-discriminative information from all subspaces are further extracted to support a better classification. Empirical extensive experiments on seven real-world data sets have demonstrated the effectiveness of DICS, and show its superiority over many state-of-the-art algorithms.

Keywords

Multi-view learning Matrix factorization Classification 

Notes

Acknowledgments

This work is supported by the National Natural Science Foundation of China (61403062, 41601025, 61433014,), Science-Technology Foundation for Young Scientist of SiChuan Province (2016JQ0007), State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering (2017490211), National key research and development program (2016YFB0502300).

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Zhong Zhang
    • 1
  • Zhili Qin
    • 1
  • Peiyan Li
    • 1
  • Qinli Yang
    • 1
  • Junming Shao
    • 1
    Email author
  1. 1.School of Computer Science and Engineering, Big Data Reserach CenterUniversity of Electronic Science and Technology of ChinaChengduChina

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