Machine learningMachine learning
可解释高斯混合模型
可解释高斯混合模型（X-GMM）在经典的 GMM 概率聚类框架中增加了透明度机制——例如特征归因分数、组件级摘要或稀疏协方差结构——以便发现的聚类和密度估计能够被人类专家理解、沟通和审计。
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来源
- Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective (Ch. 11 — Mixture Models). MIT Press. ISBN: 978-0-262-01802-9
- Gaussian mixture model. Wikipedia. link ↗
如何引用本页
ScholarGate. (2026, June 3). Explainable Gaussian Mixture Model (X-GMM). ScholarGate. https://scholargate.app/zh/machine-learning/explainable-gaussian-mixture-model
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