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Self-weighted subspace clustering via adaptive rank constrained graph embedding

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Abstract

Recent years, subspace clustering methods have attracted wide attention in partitioning high-dimensional data from a union of underlying subspaces, in which the data distribution is mainly explored to compensate for the absence of label information. However, for practical applications, subspace clustering still suffers from redundant and noisy features, which brings about disturbed reconstruction loss and restricts trustworthy graph learning. In this paper, we propose a robust subspace clustering framework via Self-weighted feature learning and adaptive rank constrained graph embedding (SWARG) to address the limitations of existing graph-based subspace clustering models. Specifically, a feature self-weighted learning term is introduced to the sparse subspace clustering framework to alleviate the disturbed contributions from the noisy and redundant features. As such, a few discriminative features will act as remarkable contributions in representing data samples. Meanwhile, the profile-based graph embedding term further preserving the contribution behavior information of data samples that distributed around the same subspace. Moreover, the adaptive rank-constraint graph embedding method is considered to guarantee discriminative structure for different components of representation matrix with flexible entropy-based similarity preserving. To solve the proposed model, we then develop an efficient alternative direction updating algorithm, together with convergence and complexity analysis. Finally, experimental results on toy databases and benchmark databases demonstrate the effectiveness of the proposed SWARG model compared to a series of state-of-the-art models. Our code is available at http://github.com/ty-kj/SAWRG.

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Data Availability

All data generated or analysed during this study are publicly available or included in this published article.

Notes

  1. Codes and Datasets for Feature Learning:http://www.cad.zju.edu.cn/home/dengcai/Data/data.html.

  2. Label Consistent K-SVD, Downloads:http://www.zhuolin.umiacs.io/projectlcksvd.html.

  3. Japanese Female Facial Expression (JAFFE) Database:http://www.kasrl.org/jaffe.html.

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Acknowledgements

This work is supported by the Scientific Research Program Funded by Education Department of Shaanxi Provincial Government (Program No. 23JP107 and 21JP081), in part by the National Natural Science Foundation of China under Grant (Program No. 62172331), in part by the Natural Science Founds of Shaanxi (Program No. 2023-YBGY-271), in part by the Natural Science Founds of Sichuan (Program No. 2022NSFSC0549).

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  1. School of Computer Science and Engineering, Xi’an University of Technology, Xi’an, China

    Kun Jiang, Zhihai Yang & Qindong Sun

  2. Sichuan Digital Economy Industry Development Research Institute, Chengdu, China

    Zhihai Yang

  3. School of Data Science and Artificial Intelligence, Chang’an University, Xi’an, China

    Zhihai Yang

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Jiang, K., Yang, Z. & Sun, Q. Self-weighted subspace clustering via adaptive rank constrained graph embedding. Pattern Anal Applic 28, 23 (2025). https://doi.org/10.1007/s10044-024-01405-6

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