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International Conference on Neural Information Processing

ICONIP 2018: Neural Information Processing pp 27–39Cite as

Robust Multi-view Features Fusion Method Based on CNMF

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11304))

Abstract

Multi-view feature fusion should be expected to mine implicit nature relationships among multiple views and effectively combine the data presented by multiple views to obtain the new feature representation of the object using a right model. In practical applications, Collective Matrix Factorization (CMF) has good effects on the fusion of multi-view data, but for noise-containing situations, the generalization ability is poor. Based on this, the paper came up with a Robust Collective Non-negative Matrix Factorization (RCNMF) model which can learn the shared feature representation of multi-view data and denoise at the same time. Based on several public data sets, experimental results fully demonstrate the effectiveness of the proposed method.

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Author information

Authors and Affiliations

  1. School of Computer Science and Technology & Joint International Research Laboratory of Machine Learning and Neuromorphic Computing, Soochow University, Suzhou, China

    Bangjun Wang, Li Zhang & Fanzhang Li

  2. School of Computer Science and Technology, Tianjin University, Tianjin, China

    Liu Yang

Authors
  1. Bangjun Wang
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  2. Liu Yang
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  3. Li Zhang
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  4. Fanzhang Li
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Corresponding author

Correspondence to Bangjun Wang .

Editor information

Editors and Affiliations

  1. The Chinese Academy of Sciences, Beijing, China

    Long Cheng

  2. City University of Hong Kong, Kowloon, Hong Kong

    Andrew Chi Sing Leung

  3. Kobe University, Kobe, Japan

    Seiichi Ozawa

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Wang, B., Yang, L., Zhang, L., Li, F. (2018). Robust Multi-view Features Fusion Method Based on CNMF. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11304. Springer, Cham. https://doi.org/10.1007/978-3-030-04212-7_3

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  • DOI: https://doi.org/10.1007/978-3-030-04212-7_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04211-0

  • Online ISBN: 978-3-030-04212-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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