Machine Learning Study Node - Clustering
Last updated: 2017-10-10 15:39:14 PDT.
In parameteric approaches, we assume the samples are drawn from the a same parametric distribution. This is rarely the case. Now we relax this assumption and assume the samples are from one of a number of distributions.
This approach is called semiparametric density estimation.
The mixture density is defined as
where are the mixture components. They are also called clusters. are called component densities and are called mixture proportions. The number of parameters is a hyperparameter.
Within each cluster is a parameteric distribution. The samples are assumed to be iid.