Machine learning
高斯混合模型
高斯混合模型（Gaussian Mixture Model, GMM）是一种概率聚类方法，它将数据建模为几个高斯分布的加权混合，并通过Dempster、Laird和Rubin于1977年形式化的期望最大化（Expectation–Maximization, EM）算法进行拟合。它是K-均值（K-means）的一种推广，其中每个簇都可以有自己的形状、大小和方向。
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来源
- Dempster, A.P., Laird, N.M. & Rubin, D.B. (1977). Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society: Series B, 39(1), 1–22. DOI: 10.1111/j.2517-6161.1977.tb01600.x ↗
如何引用本页
ScholarGate. (2026, June 1). Gaussian Mixture Model (GMM Clustering). ScholarGate. https://scholargate.app/zh/machine-learning/gaussian-mixture
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