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Enhanced Multi-view Matrix Factorization with Shared Representation

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Pattern Recognition and Computer Vision (PRCV 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 13022))

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Abstract

Multi-view data is widely used in the real world, and traditional machine learning methods are not specifically designed for multi-view data. The goal of multi-view learning is to learn practical patterns from the divergent data sources. However, most previous researches focused on fitting feature embedding in target tasks, so researchers put forward with the algorithm which aims to learn appropriate patterns in data with associative properties. In this paper, a multi-view deep matrix factorization model is proposed for feature representation. First, the model constructs a multiple input neural network with shared hidden layers for finding a low-dimensional representation of all views. Second, the quality of representation matrix is evaluated using discriminators to improve the feature extraction capability of matrix factorization. Finally, the effectiveness of the proposed method is verified through comparative experiments on six real-world datasets.

The first author is a student. This work is in part supported by the National Natural Science Foundation of China (Grant No. U1705262), the Natural Science Foundation of Fujian Province (Grant Nos. 2020J01130193 and 2018J07005).

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Notes

  1. 1.

    http://riemenschneider.hayko.at/vision/dataset/task.php?did=35.

  2. 2.

    http://lig-membres.imag.fr/grimal/data.html.

  3. 3.

    http://aloi.science.uva.nl/.

  4. 4.

    https://lms.comp.nus.edu.sg/wp-content/uploads/2019/research/nuswide.

  5. 5.

    http://yann.lecun.com/exdb/mnist/.

  6. 6.

    http://www.vision.caltech.edu/Image_Datasets/Caltech101/Caltech101.html.

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Author information

Authors and Affiliations

  1. College of Mathematics and Computer Science, Fuzhou University, Fuzhou, 350108, China

    Sheng Huang, Yunhe Zhang, Lele Fu & Shiping Wang

Authors
  1. Sheng Huang
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  2. Yunhe Zhang
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  3. Lele Fu
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  4. Shiping Wang
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Editor information

Editors and Affiliations

  1. University of Science and Technology Beijing, Beijing, China

    Huimin Ma

  2. Chinese Academy of Sciences, Beijing, China

    Liang Wang

  3. Tsinghua University, Beijing, China

    Changshui Zhang

  4. Zhejiang University, Hangzhou, China

    Fei Wu

  5. Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  6. Hunan University, Changsha, China

    Yaonan Wang

  7. Sun Yat-Sen University, Guangzhou, Guangdong, China

    Jianhuang Lai

  8. Beijing Jiaotong University, Beijing, China

    Yao Zhao

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Huang, S., Zhang, Y., Fu, L., Wang, S. (2021). Enhanced Multi-view Matrix Factorization with Shared Representation. In: Ma, H., et al. Pattern Recognition and Computer Vision. PRCV 2021. Lecture Notes in Computer Science(), vol 13022. Springer, Cham. https://doi.org/10.1007/978-3-030-88013-2_23

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  • DOI: https://doi.org/10.1007/978-3-030-88013-2_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-88012-5

  • Online ISBN: 978-3-030-88013-2

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