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Tensorized Anchor Graph Learning for Large-scale Multi-view Clustering

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Abstract

With the continuous development of information acquisition technologies, large-scale multi-view data increases rapidly. The enormous computational and storage complexity makes it very challenging to process these data in real-world applications. Most existing multi-view subspace clustering (MVSC) always suffers from quadratic space complexity and quadratic or even cubic time complexity, resulting in extreme limitations for large-scale tasks. Meanwhile, the original data usually contain lots of noise or redundant features, which further enhances the difficulty of the large-scale clustering tasks. This paper proposes a novel MVSC method for efficiently and effectively dealing with large-scale multi-view data, termed as tensorized anchor graph learning (TAGL) for large-scale multi-view clustering. Concretely, TAGL first projects the original multi-view data from the original space into the latent embedding space, where the view-consistent anchor matrix. Meanwhile, we establish the connection between the anchor matrix and the original data to construct multiple view-specific anchor graphs. Furthermore, these anchor graphs are stacked into a graph tensor to capture the high-order correlation. Finally, by developing an effective optimization algorithm, the high-quality anchors, anchor graph, and anchor graph tensor can be jointly learned in a mutually reinforcing way. Experimental results on several big sizes of datasets verify the superiority and validity of TAGL. Therefore, the proposed TAGL can efficiently and effectively handle large-scale data tasks for real-world applications.

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Data Availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Notes

  1. http://www.vision.caltech.edu/Image Datasets/Caltech101/

  2. https://www.ee.columbia.edu/ln/dvmm/CCV/

  3. https://rgbd.cs.princeton.edu/

  4. http://www.cs.toronto.edu/ kriz/cifar.html

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Acknowledgements

This work was supported by the Sichuan Science and Technology Program (project no. 2021YJ0083), the Zhejiang Provincial Natural Science Foundation of China (project no. LGF21F020003), and the Natural Science Foundation of Chongqing (project no. cstc2020jcyjmsxmX0473).

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Authors and Affiliations

  1. School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China

    Jian Dai, Hong Song & Jian Yang

  2. Southwest Automation Research Institute, China South Industries Group Corporation, Mianyang, 621000, China

    Jian Dai & Yunzhi Luo

  3. School of National Defence Science and Technology, Southwest University of Science and Technology, Mianyang, 621010, China

    Zhenwen Ren

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  1. Jian Dai
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Correspondence to Jian Yang.

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Dai, J., Ren, Z., Luo, Y. et al. Tensorized Anchor Graph Learning for Large-scale Multi-view Clustering. Cogn Comput 15, 1581–1592 (2023). https://doi.org/10.1007/s12559-023-10146-3

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