# Em Algorithm Matlab Code

The following matlab project contains the source code and matlab examples used for em algorithm.

The following matlab project contains the source code and matlab examples used for em algorithm.

The following Matlab project contains the source code and Matlab examples used for smooth point set registration using neighboring constraints.
Point-set registration methods using the EM algorithm and Softassign that exploits geometric and structural evidence.

The following Matlab project contains the source code and Matlab examples used for factor analysis.
The function implements the EM algorithm for factor analysis.

The following Matlab project contains the source code and Matlab examples used for em algorithm i.i.d. mixture distribution.
This function infers the unobserved regimes and provides estimates for the parameters of a Gaussian mixture with two states using the EM algorithm.

The following Matlab project contains the source code and Matlab examples used for em algorithm for clustering (emfc).
It works just fine, download it only if you re ok with programming. You will have to know what EM is before downloading it.

The following Matlab project contains the source code and Matlab examples used for em mvgm.
Mex implementation of EM algorithm for multivariate Gaussian mixture.

Generalized method of moments (GMM) is a generic method for estimating parameters in statistical models. Usually it is applied in the context of semiparametric models, where the parameter of interest is finite-dimensional, whereas the full shape of the distribution function of the data may not be known, and therefore the maximum likelihood estimation is not applicable.

The following Matlab project contains the source code and Matlab examples used for 3d visualization of gmm learning via the em algorithm.
This is a 3D visualization of how the Expectation Maximization algorithm learns a Gaussian Mixture Model for 3-dimensional data.

The following Matlab project contains the source code and Matlab examples used for em algorithm for gaussian mixture model with background noise.
This is the standard EM algorithm for GMMs, presented in Bishop's book "Pattern Recognition and Machine Learning", Chapter 9, with one small exception, the addition of a uniform distribution to the mixture to pick up background noise/speckle; data points which one would not want to associate with any cluster.

The following Matlab project contains the source code and Matlab examples used for em algorithm for gaussian mixture model.
This is a function tries to obtain the maximum likelihood estimation of Gaussian mixture model by expectation maximization (EM) algorithm.

**Image segmentation** is the process of partitioning a digital image into multiple segments (sets of pixels, also known as superpixels).

The following Matlab project contains the source code and Matlab examples used for image segmentation with em algorithm.
K means segmentation method has an underlying assumption that each element cannot belong to two clusters at the same time.