Machine learningMachine learning
在线高斯混合模型
在线高斯混合模型通过用增量更新替换全批量期望最大化（EM）算法，将经典GMM适应于流式或大规模数据——一次处理一个观测值或一个小批量数据，并持续优化组件均值、协方差和混合权重，而无需重新访问整个数据集。
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来源
- Cappé, O. & Moulines, E. (2009). On-line expectation-maximization algorithm for latent data models. Journal of the Royal Statistical Society: Series B, 71(3), 593–613. DOI: 10.1111/j.1467-9868.2009.00698.x ↗
- Sato, M. & Ishii, S. (2000). On-line EM algorithm for the normalized Gaussian network. Neural Computation, 12(2), 407–432. DOI: 10.1162/089976600300015853 ↗
如何引用本页
ScholarGate. (2026, June 3). Online Gaussian Mixture Model (Incremental / Streaming GMM). ScholarGate. https://scholargate.app/zh/machine-learning/online-gaussian-mixture-model
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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