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Multi-view Restricted Boltzmann Machines with Posterior Consistency

  • Ding Shifei
  • Zhang NanEmail author
  • Zhang Jian
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 538)

Abstract

Restricted Boltzmann machines (RBMs) have been proven to be powerful tools in many specific applications, such as representational learning and document modelling. However, the extensions of RBMs are rarely used in the field of multi-view learning. In this paper, we present a new multi-view RBM model, named as the RBM with posterior consistency, for multi-view classification. The RBM with posterior consistency computes multiple representations by regularizing the marginal likelihood function with the consistency among representations from different views. Contrasting with existing multi-view classification methods, such as multi-view Gaussian pro-cess with posterior consistency (MvGP) and consensus and complementarity based maximum entropy discrimination (MED-2C), the RBM with posterior consistency have achieved satisfactory results on two-class and multi-class classification datasets.

Keywords

Restricted Boltzmann machines Representational learning Multi-view learning 

Notes

Acknowledgements

This work is supported by the National Natural Science Foundation of China under Grant no. 61672522 and no. 61379101.

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Copyright information

© IFIP International Federation for Information Processing 2018

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyChina University of Mining and TechnologyXuzhouChina

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