Machine learning
均值漂移
均值漂移（Mean Shift）是一种非参数的、迭代式的模式搜索算法，它将聚类识别为底层概率密度函数峰值。该算法最初由 Fukunaga 和 Hostetler (1975) 提出，用于模式识别中的梯度估计，后由 Comaniciu 和 Meer (2002) 大幅扩展并推广，用于稳健的特征空间分析和图像分割。与 K-means 不同，均值漂移无需预先指定聚类数量，而是完全从数据密度中导出聚类结构。
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来源
- Fukunaga, K. & Hostetler, L. D. (1975). The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Transactions on Information Theory, 21(1), 32–40. DOI: 10.1109/TIT.1975.1055330 ↗
- Comaniciu, D. & Meer, P. (2002). Mean shift: A robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(5), 603–619. DOI: 10.1109/34.1000236 ↗
- Hastie, T., Tibshirani, R. & Friedman, J. (2009). The Elements of Statistical Learning (2nd ed., Ch. 14). Springer. ISBN: 978-0-387-84858-7
如何引用本页
ScholarGate. (2026, June 3). Mean Shift Clustering and Mode-Seeking Algorithm. ScholarGate. https://scholargate.app/zh/machine-learning/mean-shift
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