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Low-rank tensor multi-view subspace clustering via cooperative regularization

  • 1227: Content-based Image Retrieval
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Abstract

In order to explore the importance of the hypergraph regularization and the Tikhonov regularization in multi-view clustering, this paper proposes a novel multi-view clustering model, termed as low-rank tensor multi-view subspace clustering via collaborative regularization (LT-MSCCR). The LT-MSCCR model introduces the idea of tensor. The tensor is designed to represent the result of superimposing the subspace representation matrix, which is used to explore the high order correlations between different views, and the low-rank restriction is performed for this tensor to effectively reduce redundant information. Furthermore, we adopt the hypergraph regularization to mine effective geometric information in multi-view data. Meanwhile, we also impose the Tikhonov regularization constraint on the subspace representation matrix so as to improve the smoothness of the subspace representation matrix and enhance the recognition performance of the proposed method. In addition, we also designed a valuable approach to optimizing the proposed model and theoretically analyzing the convergence of the LT-MSCCR method. The experimental results on some datasets show that the proposed model is better than many advanced multi-view clustering methods.

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Notes

  1. https://www.cnblogs.com/picassooo/p/12890078.html

  2. https://download.csdn.net/download/wuwulala22/7210241

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

  4. http://vision.ucsd.edu/~leekc/ExtYaleDatabase/ExtYaleB.html

  5. http://vision.ucmerced.edu/datasets

  6. http://www-cvr.ai.uiuc.edu/ponce_grp/data/

  7. http://mlg.ucd.ie/datasets/3sources. html

  8. http://mlg.ucd.ie/datasets/bbc.html

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Acknowledgments

The National Natural Science Foundation of China under Grants 61806006, China Postdoctoral Science Foundation under Grant No. 2019 M660149, the 111 Project under Grants No. B12018, and PAPD of Jiangsu Higher Education Institutions support this work.

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Authors and Affiliations

  1. School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, 214122, Jiangsu, China

    Guoqing Liu, Hongwei Ge & Shuangxi Wang

  2. Key Laboratory of Advanced Process Control for Light Industry (Jiangnan University), Ministry of Education, Wuxi, 214122, China

    Guoqing Liu, Hongwei Ge & Shuangxi Wang

  3. School of Computer Science and Engineering, Anhui University of Science & Technology, Huainan, 232001, Anhui, China

    Shuzhi Su

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Correspondence to Hongwei Ge.

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Liu, G., Ge, H., Su, S. et al. Low-rank tensor multi-view subspace clustering via cooperative regularization. Multimed Tools Appl 82, 38141–38164 (2023). https://doi.org/10.1007/s11042-022-14298-6

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