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高斯混合模型

使用期望最大化算法,基于高斯混合模型进行聚类

高斯混合模型 (GMM) 按以下原则将每个观测值分配给簇:使观测值属于其所分配给的簇的后验概率最大。可通过对数据进行模型拟合 (fitgmdist) 或通过指定参数值 (gmdistribution) 来创建 GMM 对象 gmdistribution。然后使用对象函数执行聚类分析(clusterposteriormahal)、计算模型(cdfpdf),并生成随机变量 (random)。

函数

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fitgmdistFit Gaussian mixture model to data
gmdistributionCreate Gaussian mixture model
cdfCumulative distribution function for Gaussian mixture distribution
clusterConstruct clusters from Gaussian mixture distribution
mahalMahalanobis distance to Gaussian mixture component
pdfProbability density function for Gaussian mixture distribution
posteriorPosterior probability of Gaussian mixture component
randomRandom variate from Gaussian mixture distribution

主题

Cluster Using Gaussian Mixture Model

Partition data into clusters with different sizes and correlation structures.

Cluster Gaussian Mixture Data Using Hard Clustering

Implement hard clustering on simulated data from a mixture of Gaussian distributions.

Cluster Gaussian Mixture Data Using Soft Clustering

Implement soft clustering on simulated data from a mixture of Gaussian distributions.

Tune Gaussian Mixture Models

Determine the best Gaussian mixture model (GMM) fit by adjusting the number of components and the component covariance matrix structure.