An EM algorithm for singular Gaussian mixture models

Gaussian mixture models and the EM algorithm

These notes give a short introduction to Gaussian mixture models (GMMs) and the Expectation-Maximization (EM) algorithm, first for the specific case of GMMs, and then more generally. These notes assume you’re familiar with basic probability and basic calculus. If you’re interested in the full derivation (Section 3), some familiarity with entropy and KL divergence is useful but not strictly requ...

A robust EM clustering algorithm for Gaussian mixture models

Clustering is a useful tool for finding structure in a data set. The mixture likelihood approach to clustering is a popular clustering method, in which the EM algorithm is the most used method. However, the EM algorithm for Gaussian mixture models is quite sensitive to initial values and the number of its components needs to be given a priori. To resolve these drawbacks of the EM, we develop a ...

Random swap EM algorithm for Gaussian mixture models

0167-8655/$ see front matter 2012 Elsevier B.V. A http://dx.doi.org/10.1016/j.patrec.2012.06.017 ⇑ Corresponding author. Tel.: +358 132517962. E-mail address: [email protected] (Q. Zhao). Expectation maximization (EM) algorithm is a popular way to estimate the parameters of Gaussian mixture models. Unfortunately, its performance highly depends on the initialization. We propose a random swap EM...

Competitive EM algorithm for finite mixture models

In this paper, we present a novel competitive EM (CEM) algorithm for "nite mixture models to overcome the two main drawbacks of the EM algorithm: often getting trapped at local maxima and sometimes converging to the boundary of the parameter space. The proposed algorithm is capable of automatically choosing the clustering number and selecting the “split” or “merge” operations e7ciently based on...

A greedy EM algorithm for Gaussian mixture learning CONTENTSContents