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Fuzzy Bilinear Latent Canonical Correlation Projection for Feature Learning

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Neural Information Processing (ICONIP 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11953))

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Abstract

Canonical correlation analysis (CCA) is a widely used linear unsupervised subspace learning method. However, standard CCA works with vectorized representation of image matrix, which loses the spatial structure information of image data. In addition, a real-world observation often simultaneously belongs to multiple distinct classes with different degrees of membership, while conventional CCA methods can not deal with this situation. Inspired by aforementioned issues, we in this paper propose a fuzzy bilinear canonical correlation projection (FBCCP) approach. FBCCP not only considers two-dimensional spatial structure of images, but also membership degree of practical observation belonging to different classes at the same time. Experimental results on visual recognition show that the proposed FBCCP approach is more effective than related feature learning approaches.

Supported by Undergraduate Education and Teaching Reform Project of Yangzhou University under Grant YZUJX2016-32C; National Natural Science Foundation of China under Grants 61402203, 61703362, and 61611540347; Natural Science Foundation of Jiangsu Province of China under Grants BK20161338 and BK20170513; Yangzhou Science Project Fund of China under Grants YZ2016238 and YZ2017292; Excellent Young Backbone Teacher (Qing Lan) Project and Scientific Innovation Project Fund of Yangzhou University of China under Grant 2017CXJ033.

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Notes

  1. 1.

    https://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html

  2. 2.

    http://www2.ece.ohio-state.edu/~aleix/ARdatabase.html

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Author information

Authors and Affiliations

  1. School of Information Engineering, Yangzhou University, Yangzhou, China

    Yun-Hao Yuan, Hui Zhang, Yun Li, Jipeng Qiang & Bin Li

  2. School of Computer Science and Technology, Fudan University, Shanghai, China

    Yun-Hao Yuan

  3. School of Computer Science, Jiangsu University, Zhenjiang, China

    Jianping Gou

  4. Institute of Advanced Technology, Nanjing University of Posts and Telecommunications, Nanjing, China

    Guangwei Gao

Authors
  1. Yun-Hao Yuan
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  2. Hui Zhang
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  3. Yun Li
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  4. Jipeng Qiang
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  5. Jianping Gou
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  6. Guangwei Gao
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  7. Bin Li
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Corresponding author

Correspondence to Yun-Hao Yuan .

Editor information

Editors and Affiliations

  1. Australian National University, Canberra, ACT, Australia

    Tom Gedeon

  2. Murdoch University, Murdoch, WA, Australia

    Kok Wai Wong

  3. Kyungpook National University, Daegu, Korea (Republic of)

    Minho Lee

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Yuan, YH. et al. (2019). Fuzzy Bilinear Latent Canonical Correlation Projection for Feature Learning. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Lecture Notes in Computer Science(), vol 11953. Springer, Cham. https://doi.org/10.1007/978-3-030-36708-4_55

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  • DOI: https://doi.org/10.1007/978-3-030-36708-4_55

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-36707-7

  • Online ISBN: 978-3-030-36708-4

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