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Authors
Yu, Shuyan
Deng, Hao
Liu, Xinyu
Zheng, Yang
Liu, Zhankun
Chen, Jin
Mao, Xiancheng
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Funding
Funding for this research was provided by:
National Major Science and Technology Projects of China (2024ZD1001904)
National Key Technology Research and Development Program (No. 2023YFC2906403)
National Natural Science Foundation of China (No. 42272344)
National Natural Science Foundation of China (No.42030809)
National Natural Science Foundation of China (No.72088101)
National Natural Science Foundation of China (No. 41972309)
Natural Science Foundation of Hunan Province (No. 2024JJ8323)
the Science and Technology Innovation Program of Hunan Province (2021RC4055)
Fundamental Research Funds for Central Universities of the Central South University (No. 2024ZZTS0362)
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Article History
Received: 14 July 2025
Accepted: 28 September 2025
First Online: 14 November 2025
Declarations
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: We declare that we have no financial or personal relationships with other people or organizations that could inappropriately influence our work, and there is no professional or other personal interest of any nature or kind in any product, service, and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled “Expectation–Maximization-Derived Self-distillation Meets Transformer: A Robust Unsupervised Deep Learning Approach for Geochemical Anomaly Recognition.”
