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Multi-view clustering with dual tensors

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Abstract

Multi-view clustering methods based on tensor have achieved favorable performance thanks to the powerful capacity of capturing the high-order correlation hidden in multi-view data. However, many existing works only pay attention to exploring the inter-view correlation (i.e., the correlation between views for a same sample) and ignore the intra-view correlation (i.e., the correlation between different samples in a view), such that the high-order information cannot be fully utilized. Toward this issue, we propose an innovative multi-view clustering method in this paper, multi-view clustering with dual tensors (MCDT), which simultaneously exploits the intra-view correlation and the inter-view correlation. Specifically, we first learn a set of specific affinity matrices by using subspace learning in each view. Then, we stack these affinity matrices into a tensor and impose the tensor nuclear norm to exploit the intra-view high-order correlation. Meanwhile, we also rotate this tensor to exploit the inter-view high-order correlation, so as to exploit more comprehensive information hidden in multiple views. Extensive experiments on benchmark datasets demonstrate that the proposed MCDT obtains superior performance in comparison with existing state-of-the-art methods.

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Acknowledgements

This work was supported by the Sichuan Science and Technology Program (Grant no. 2021YJ0083), the State Key Lab. Foundation for Novel Software Technology of Nanjing University (Grant no. KFKT2021B23), the Sichuan Science and Technology Miaozi Program (Grant no. 2021020), the National Statistical Science Research Project (Grant no. 2020491), the Postgraduate Innovation Fund Project of Southwest University of Science and Technology (Grant no. 20ycx0055), the Guangxi Natural Science Foundation (Grant no. 2020GXNSFAA297186), and the Guangxi Science and Technology Major Project (Grant no. 2018AA32001).

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Authors and Affiliations

  1. Department of Information Engineering, Southwest University of Science and Technology, Mianyang, 621010, China

    Yong Mi & Haoran Li

  2. Department of National Defence Science and Technology, Southwest University of Science and Technology, Mianyang, 621010, China

    Zhenwen Ren

  3. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210008, China

    Zhenwen Ren

  4. Guangxi Key Laboratory of Images and Graphics Intelligent Processing, Guilin University of Electronics Technology, Guilin, 541004, China

    Zhi Xu

  5. Department of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China

    Quansen Sun

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

    Hongxia Chen & Jian Dai

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  1. Yong Mi
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Correspondence to Zhenwen Ren.

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Mi, Y., Ren, Z., Xu, Z. et al. Multi-view clustering with dual tensors. Neural Comput & Applic 34, 8027–8038 (2022). https://doi.org/10.1007/s00521-022-06927-w

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