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I want to use Gaussian mixture models for data clustering ( using an expectation maximization (EM) algorithm, which assigns posterior probabilities to each component density with respect to each observation ) . Is there a c++ library which has Gaussian mixture models implemented alongwith sample dataset and examples?
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In statistics, a mixture model is a probabilistic model for representing the presence of subpopulations within an overall population, without requiring that an observed data set should identify the sub-population to which an individual observation belongs.
GMM considers each cluster as a different Gaussian distribution. Then it will tell, based on probability, out of which distribution that data point came out. Probably the most known and used algorithm for clustering is K-Means. But it has its limitations.
Gaussian mixture models (GMMs) are often used for data clustering. You can use GMMs to perform either hard clustering or soft clustering on query data. To perform hard clustering, the GMM assigns query data points to the multivariate normal components that maximize the component posterior probability, given the data.
K-Means and Gaussian Mixture Model (GMM) are unsupervised clustering techniques. K-Means groups data points using distance from the cluster centroid [8] - [16]. GMM uses a probabilistic assignment of data points to clusters [17] - [19]. Each cluster is described by a separate Gaussian distribution.
The Armadillo C++ library has a multi-threaded (parallelised) implementation of k-means and Expectation Maximization (EM) for Gaussian Mixure Models (GMM).
See the gmm_diag class for more information.
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