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Multi-view Clustering with Latent Low-rank Proxy Graph Learning

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Abstract

With advances in information acquisition technologies, multi-view data are increasing dramatically in a variety of real-world applications, whereas such data is usually corrupted by noises and outliers. Many existing multi-view graph clustering (MVGC) methods usually learn a consensus affinity graph using a late-fusion scheme in semantic space, which compound the challenge of leveraging the underlying relationships among corrupted multi-view data. In this paper, we propose a novel clustering method for handing corrupted multi-view data, hereafter referred to as Latent Low-Rank Proxy Graph Learning (LLPGL). Specifically, by projecting the multi-view data into a low-dimension proxy feature space, LLPGL can learn a low-dimension yet low-rank latent proxy from corrupted view data. Meanwhile, by employing the adaptive neighbor graph learning over the clean proxy, a high-quality affinity graph can be learned for clustering purpose. Then, an effective optimization algorithm is proposed to solve the model of LLPGL. Experimental results on five widely used real-world benchmarks validate the effectiveness of the proposed method.Consequently, the proposed method can be used to cluster the corrupted multi-view data for real-life applications.

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Notes

  1. The late-fusion scheme fuses multi-view information in semantic space, while the early-fusion scheme fuses multi-view information in feature space.

  2. https://canyilu.github.io/publications/2015-report-simplex.pdf

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Acknowledgements

This work was supported by the Sichuan Science and Technology Program (Grant no. 2021YJ0083), the Zhejiang Provincial Natural Science Foundation of China (Grant no. LGF21F020003), the Natural Science Foundation of Chongqing (Grant no. cstc2020jcyjmsxmX0473), and the National Statistical Science Research Project (Grant no. 2020491).

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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

  4. School of Electronic and Information Engineering, Chongqing Three Gorges University, Chongqing, 404199, China

    Zhenwen Ren

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  1. Jian Dai
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  3. Yunzhi Luo
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  4. Hong Song
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Correspondence to Jian Yang.

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Dai, J., Ren, Z., Luo, Y. et al. Multi-view Clustering with Latent Low-rank Proxy Graph Learning. Cogn Comput 13, 1049–1060 (2021). https://doi.org/10.1007/s12559-021-09889-8

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  • DOI: https://doi.org/10.1007/s12559-021-09889-8

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