Is Gaussian mixture model supervised?

The traditional Gaussian Mixture Model (GMM) for pattern recognition is an unsupervised learning method. The Supervised Learning Gaussian Mixture Model (SLGMM) improves the recognition accuracy of the GMM. An experimental example has shown its effectiveness.

What is covariance in Gaussian mixture?

The covariance matrix of a Gaussian distribution determines the directions and lengths of the axes of its density contours, all of which are ellipsoids. These four types of mixture models can be illustrated in full generality using the two-dimensional case.

What is Gaussian Mixture Modeling?

Gaussian mixture models (GMMs) assign each observation to a cluster by maximizing the posterior probability that a data point belongs to its assigned cluster. Create a GMM object gmdistribution by fitting a model to data ( fitgmdist) or by specifying parameter values ( gmdistribution ).

Is there a vectorized version of the expectation maximization algorithm?

This post serves as a practical approach towards a vectorized implementation of the Expectation Maximization (EM) algorithm mainly for MATLAB or OCTAVE applications. EM is a really powerful and elegant method for finding maximum likelihood solutions in cases where the hypothesis involves a gaussian mixture model and latent variables.

How to perform soft clustering on a mixture of Gaussian distributions?

Implement soft clustering on simulated data from a mixture of Gaussian distributions. Determine the best Gaussian mixture model (GMM) fit by adjusting the number of components and the component covariance matrix structure. Run the command by entering it in the MATLAB Command Window.

What are the means and covariances of a Gaussian mixture?

Suppose the observations are drawn from a gaussian mixture model with some unknown mixture coefficients, means and covariances. The means can be cell arrays of elements that are vectors, one for each of the gaussians in the mixture.