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Robust multi-view low-rank embedding clustering

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Abstract

Significant improvements of multi-view subspace clustering have emerged in recent years. However, multi-view data are often lying on high-dimensional space and inevitably corrupted by noise and even outliers, which pose challenges for fully exploiting the intrinsic underlying relevance of multi-view data, as the redundant and corrupted features are highly deceptive. To address the above problems, this paper proposes a robust multi-view low-rank embedding (RMLE) method for clustering. Specifically, RMLE projects each high-dimensional view onto a clean low-rank embedding space without energy loss, such that multiple high-quality candidate affinity graphs are yielded by using self-expressiveness subspace learning. Meanwhile, it integrates the clean complimentary information of multi-view data in semantic space to learn a shared consensus affinity graph. Further, an efficient alternating optimization algorithm is designed to solve our RMLE by the alternating direction method of multipliers. Extensive experiments on four benchmark multi-view datasets demonstrate the performance superiority and advantages of RMLE against many state-of-the-art clustering methods.

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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. Affinity matrix is deemed as the coefficient matrix \({\bf{Z}}\) of SEP, which is also referred to as affinity graph in graph semantic space.

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Acknowledgements

This work was supported by the Project of Key Laboratory of System Control and Information Processing (Grant No. Scip202210), and the Open Project Program of the State Key Lab of CAD &CG of Zhejiang University (Grant No. A2217).

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

    Zhenwen Ren

  3. Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, 200240, China

    Zhenwen Ren

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

    Jian Dai, Yunzhi Luo & Zhenwen Ren

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  1. Jian Dai
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  2. Hong Song
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  3. Yunzhi Luo
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  4. Zhenwen Ren
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  5. Jian Yang
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Correspondence to Zhenwen Ren or Jian Yang.

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Dai, J., Song, H., Luo, Y. et al. Robust multi-view low-rank embedding clustering. Neural Comput & Applic 35, 7877–7890 (2023). https://doi.org/10.1007/s00521-022-08137-w

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