混合模型:以概论为基础的‘软聚类(soft clustering), 每一个聚类是一个生成模型(generative model)即学习模型参数比如多维高斯模型,学习的是模型的均值、协方差。对比‘硬聚类(hard clustering)比如k-mean…
高斯混合模型(Gaussian Mixture Model)是机器学习中一种常用的聚类算法,本文介绍了其原理,并推导了其参数估计的过程。主要参考Christopher M. Bishop的《Pattern Recognition and Machine Learning》。 以粗体小写字母表示向量,粗体大写字母表示矩阵;标量不加粗,大写表示常数。 1. 高斯分布 高斯分布(Gaussian distribution)...
Gaussian Mixture Model (GMM) is a popular clustering algorithm due to its neat statistical properties, which enable the "soft" clustering and the determination of the number of clusters. Expectation-Maximization (EM) is usually applied to estimate the GMM parameters. While promising, the inclusion ...
最近在看晓川老(shi)师(shu)的博士论文,接触了混合高斯模型(Gaussian mixture model, GMM)和EM(Expectation Maximization)算法,不禁被论文中庞大的数学公式所吓退。本文通过查阅相关资料,在复杂巧妙的推理公式中融入了自己的理解,详细梳理了混合高斯模型和EM算法。 1 单高斯模型(Gaussian single model, GSM) 简单回顾...
【转】详解EM算法与混合高斯模型(Gaussian mixture model, GMM) 【转】详解EM算法与混合高斯模型(Gaussian mixture model, GMM) 转载自:https://blog.csdn.net/lin_limin/article/details/81048411 作者:林立民爱洗澡 觉得有用的话,欢迎一起讨论相互学习~...
#Train the other parameters using the EM algorithm.classifier.fit(X_train) # 数据表现h= plt.subplot(2, n_classifiers / 2, index + 1)make_ellipses(classifier, h) forn, colorinenumerate('rgb'): data= iris.data[iris.target ==n]plt.scatter(data[:, 0], data[:,1], 0.8, color=color...
and if the value taken by is implicitly given by the context. The EM algorithm Since we are able to write the Gaussian mixture model as a latent-variable model, we can use theEM algorithmto find the maximum likelihood estimators of its parameters. ...
Specifically, each learning posture is described based on its movement features by a set of spatial-temporal interest points (STIPs), salient postures are then clustered from these training samples by an unsupervised algorithm, here we give the comparison of four candidate classification methods and ...
Gaussian mixture model is a distribution based clustering algorithm. How gaussian mixture models work and how to implement in python.
In this paper we present the Gaussian mixture method to model the loss distribution of data from motor compulsory third part liability insurance. The parameters of the mixture are estimated using the Expectation Maximization (EM) algorithm.Teodorescu Sandra...