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Density peak clustering using tensor network

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Conclusion

We introduce a density-based clustering algorithm with tensor networks. In order to demonstrate its effectiveness, we apply it to various types of data sets, including synthetic data sets, real world data sets, and computer vision data sets. Results demonstrate that it is an efficient quantum-inspired unsupervised learning algorithm and can recognize clusters of arbitrary shape and size. It can also be seen that large quantum entanglement tends to provide better clustering results.

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References

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Acknowledgements

This work was supported by National Key R&D Program of China (Grant No. 2023YFA1009403), National Natural Science Foundation Special Project of China (Grant No. 12341103), and National Natural Science Foundation of China (Grant No. 62372444).

Author information

Authors and Affiliations

  1. Institute of Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China

    Xiao Shi & Yun Shang

  2. School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China

    Xiao Shi

  3. National Center for Mathematics and Interdisciplinary Sciences, Key Laboratory of Management, Decision and Information Systems, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China

    Yun Shang

Authors
  1. Xiao Shi
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  2. Yun Shang
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Corresponding author

Correspondence to Yun Shang.

Additional information

Supporting information Appendixes A–D. The supporting information is available online at info.scichina.com and link.springer.com. The supporting materials are published as submitted, without typesetting or editing. The responsibility for scientific accuracy and content remains entirely with the authors.

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Shi, X., Shang, Y. Density peak clustering using tensor network. Sci. China Inf. Sci. 67, 139404 (2024). https://doi.org/10.1007/s11432-023-3869-3

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  • DOI: https://doi.org/10.1007/s11432-023-3869-3

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