第九章Mixture Models and EM,主要内容有:Kmeans算法;混合高斯模型以及EM(Expectation Maximization)算法在GMM中的应用;一般EM算法性质的推导和证明。
C. M. Bishop, "Mixture models and EM," in Pattern Recognition and Machine Learning, pp. 423-459, New York, NY: Springer, 2006.C. M. Bishop, "Mixture Models and EM," in Pattern Recognition and Machine Learning, 1 ed: Springer-Verlag New York, Inc., 2006, pp. 423 - 460....
PRML Chapter 9. Mixture Models and EM 今天从网上搜的 EM 算法的原始论文叫做 Maximum likelihood from incomplete data via the EM algorithm,下次仔细学习的时候可以看看,最近比较功利,就不弄得那么明白了。(2012@3@21) 9.1 K-means Clustering 主要介绍了 K-means 和 EM 算法之间的关系,第一次听说原来 K-...
Repeat--based on current Guassian models, reassign labels to points Stop when no changes Inclusion K-means is a special case of GMM--all Gaussians are spherical and have identical weights and covariance (the only parameter is mean). Extension In general, EM can be used to learn any model ...
Gaussian mixture models with equivalence constraints Summary: Gaussian Mixture Models (GMMs) have been widely used to cluster data in an unsupervised manner via the Expectation Maximization (EM) algorithm. In this chapter we suggest a semi-supervised EM algorithm that incorporates equivale... N S...
There has been extensive research on finite normal mixture models, but much of it addresses merely consistency of the point estimation or useful practical procedures, and many results require undesirable restrictions on the parameter space. We show that an EM-test for homogeneity is effective at ...
Biernacki, C., Celeux, G. and Govaert, G., 2003. Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models. Computational Statistics & Data Analysis, 41(3-4), pp.561-575. ...
Learning Gaussian Mixture Models With Entropy-Based Criteria In this paper, we address the problem of estimating the parameters of Gaussian mixture models. Although the expectation-maximization (EM) algorithm yields ... AP Benavent,FE Ruiz,JM Saez - 《IEEE Trans Neural Netw》 被引量: 46发表: ...
Clustering and segmentation of heterogeneous functional data (sequential data) with regime changes by mixture of Hidden Markov Model Regressions (MixFHMMR) and the EM algorithm data-science statistical-learning regression artificial-intelligence unsupervised-learning em-algorithm hidden-markov-models time-seri...
We validate the hierarchical mixture of GPFRs and MCMC EM algorithm using synthetic and real-world data sets. Our results show that our new model outperforms the conventional mixture models in curve clustering and prediction. 展开 关键词: Curve clustering and prediction EM algorithm Gaussian process...