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Metric learning with clustering-based constraints

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Abstract

In most of the existing metric learning methods, the relation is fixed throughout the metric learning process. However, the fixed relation may be harmful to learn a good metric. The adversarial metric learning implements a dynamic update of the pairwise constraints. Inspired by the idea of dynamically updating constraints, we propose in this paper a metric learning model with clustering-based constraints (ML-CC), wherein the triple constraints of large margin are iteratively generated with the clusters of data points. The proposed method can overcome the shortage of the fixed triple constraints constructed under the Euclidian distance. The experimental results on synthetic and real datasets indicate that the performance of the ML-CC is superior to that of the existing state-of-the-art metric learning methods.

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Notes

  1. https://github.com/array12138/metric-leanring.

  2. http://cbcl.mit.edu/software-datasets/heisele/facerecognition-database.html.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. 61976184, 62006147, 61772323), the Projects of Key Research and Development Plan of Shanxi Province (201903D121162) and the 1331 Engineering Project of Shanxi Province, China.

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Authors and Affiliations

  1. Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, Shanxi, China

    Xinyao Guo, Chuangyin Dang, Jianqing Liang, Wei Wei & Jiye Liang

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  1. Xinyao Guo
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  2. Chuangyin Dang
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  3. Jianqing Liang
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  4. Wei Wei
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  5. Jiye Liang
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Correspondence to Chuangyin Dang.

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Guo, X., Dang, C., Liang, J. et al. Metric learning with clustering-based constraints. Int. J. Mach. Learn. & Cyber. 12, 3597–3605 (2021). https://doi.org/10.1007/s13042-021-01408-3

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  • DOI: https://doi.org/10.1007/s13042-021-01408-3

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