From EM To VB-GMM

1. Introduction When we use K-Means or GMM to solve clustering problem, the most important hyperparameter is the number of the cluster. It is quite hard to decide and cause the good/bad performance significantly. In the mean time, K-Means also cannot handle unbalanced dataset well. However, the variational Bayesian Gaussian mixture model(VB-GMM) can solve these. VB-GMM is a Bayesian model that contains priors over the parameters of GMM. Thus, VB-GMM can be optimized by variational Bayesian expectation maximization(VBEM) and find the optimal cluster number automatically....

Note: the code in R is on my Github 3. Variational Bayesian Gaussian Mixture Model(VB-GMM) 3.1 Graphical Model Gaussian Mixture Model & Clustering The variational Bayesian Gaussian mixture model(VB-GMM) can be represented as the above graphical model. We see each data point as a Gaussian mixture distribution with $K$ components. We also denote the number of data points as $N$. Each $x_n$ is a Gaussian mixture distribution with a weight $\pi_n$ corresponds to a data point....