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Unsupervised Maximum Margin Incomplete Multi-view Clustering

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Artificial Intelligence (ICAI 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 888))

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Abstract

Discarding incomplete multi-view instances in conventional multi-view algorithms leads to a severe loss of available information. To make up for this loss, learning from multi-view incomplete data has attracted much attention. With the goal of better clustering, the Unsupervised Maximum Margin Incomplete Multi-view Clustering (UMIMC) algorithm is proposed in this paper. Different from the existing works that simply project data into a common subspace, discriminative information is incorporated into the unified representation by applying the unsupervised maximum margin criterion. Thus, the margin between different classes is enlarged in the learned subspace, leading to improvement in the clustering performance. An alternating iterative algorithm with guaranteed convergence is developed for optimization. Experimental results on several datasets verify the effectiveness of the proposed method.

This work is supported by NSF China (No. 61473302, 61503396). Chenping Hou is the corresponding author.

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Notes

  1. 1.

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

  2. 2.

    http://www.cad.zju.edu.cn/home/dengcai/Data/FaceData.html.

  3. 3.

    http://vision.ucsd.edu/content/yale-face-database.

  4. 4.

    https://www.microsoft.com/en-us/research/project/imageunderstanding/.

  5. 5.

    http://archive.ics.uci.edu/ml/datasets/Multiple+Features.

  6. 6.

    http://www.cs.cmu.edu/~webkb/.

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Author information

Authors and Affiliations

  1. National University of Defense Technology, Changsha, 410073, Hunan, China

    Hong Tao, Chenping Hou, Dongyun Yi & Jubo Zhu

Authors
  1. Hong Tao
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  2. Chenping Hou
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  3. Dongyun Yi
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  4. Jubo Zhu
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Corresponding author

Correspondence to Chenping Hou .

Editor information

Editors and Affiliations

  1. Nanjing University, Nanjing, China

    Zhi-Hua Zhou

  2. Hong Kong University of Science and Technology, Hong Kong SAR, China

    Qiang Yang

  3. Nanjing University, Nanjing, China

    Yang Gao

  4. JD Finance, Beijing, China

    Yu Zheng

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Tao, H., Hou, C., Yi, D., Zhu, J. (2018). Unsupervised Maximum Margin Incomplete Multi-view Clustering. In: Zhou, ZH., Yang, Q., Gao, Y., Zheng, Y. (eds) Artificial Intelligence. ICAI 2018. Communications in Computer and Information Science, vol 888. Springer, Singapore. https://doi.org/10.1007/978-981-13-2122-1_2

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  • DOI: https://doi.org/10.1007/978-981-13-2122-1_2

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-2121-4

  • Online ISBN: 978-981-13-2122-1

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