高斯混合模型的graphical model 变量\mathbf{z}称为隐变量(latent variable),包含(\mathbf{x,z})取值的样本称为完全数据(complete data),只含有\mathbf{x}取值的样本称为不完全数据(incomplete data), 给定\mathbf{z}后\mathbf{x}的条件概率密度为: p(\mathbf{x} | z_k=1) = p\left(\mathbf{x | \m...
we have to understand a method called Expectation-Maximization (EM) algorithm. The EM algorithm is widely used for parameter estimation when a model depends on some unobserved latent variables. The latent variable in the Gaussian mixture model is that describes which Gaussian component a data...
Gaussian mixture modeling with Gaussian process latent variable models - Nickisch, Rasmussen - 2010 () Citation Context ...d as an extension of the Gaussian process latent variable model (GPLVM) [8]. The GPLVM is not typically thought of as a density model, but it does in fact define a ...
Density modeling is notoriously difficult for high dimensional data. One approach to the problem is to search for a lower dimensional manifold which captures the main characteristics of the data. Recently, the Gaussian Process Latent Variable Model (GPLV
Learning the model If the count of componentskis known, the EM is the method used frequently for evaluating the mixture model parameters. In frequentist probability theory the models are learned consideringmaximum likelihood estimationmethod which seems to increase the likelihood or probability of the ...
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. Starting from an initial guess of the parameter vector , the algorithm produces a new estimate of the parameter vector ...
Mixture modelGaussian processes (GPs) constitute one of the most important Bayesian machine learning approaches. Several researchers have considered postulating mixtures of Gaussian processes as a means of dealing with non-stationary covariance functions, discontinuities, multi-modality, and overlapping ...
There comes the EM algorithm. Let see how the EM algorithm is used in the Gaussian mixture model. Maximum Likelihood Estimation (MLE) can be simplified by introducing the Latent variable. A latent variable model makes the assumption that an observation xi is caused by some underlying l...
这一章开始介绍高斯混合模型GMM(Gaussian Mixture Model) 这里的高斯指高斯分布,混合是指有多个高斯分布混合在一起,比如图1-1中,有两个高斯分布,这是一组一维数据,纵轴表示概率,此图也是概率密度函数图。其中蓝色的曲线代表每一个高斯分布的概率密度,红色曲线代表两个高斯模型叠加而成的混合概率密度。
机器学习过程一般包括两步:(1)建模构建、目标优化;(2)求解,求解目标优化 在此前一文中,广义的隐变量模型已经陈述了 huangzhengxiang:机器学习入门(17)隐变量模型 Latent Variable (在表象的背后,总有…