MixEst: An Estimation Toolbox for Mixture Models

Abstract

Mixture models are powerful statistical models used in many applications ranging from density estimation to clustering and classification. When dealing with mixture models, there are many issues that the experimenter should be aware of and needs to solve. The MixEst toolbox is a powerful and user-friendly package for MATLAB that implements several state-of-the-art approaches to address these problems. Additionally, MixEst gives the possibility of using manifold optimization for fitting the density model, a feature specific to this toolbox. MixEst simplifies using and integration of mixture models in statistical models and applications. For developing mixture models of new densities, the user just needs to provide a few functions for that statistical distribution and the toolbox takes care of all the issues regarding mixture models. MixEst is available at visionlab.ut.ac.ir/mixest and is fully documented and is licensed under GPL.

5 pages

References in corpus (1)

• Manifold Optimization for Gaussian Mixture Models

Cited by in corpus (6)

• On Riemannian Optimization over Positive Definite Matrices with the Bures-Wasserstein Geometry
• A universal framework for learning the elliptical mixture model
• A Riemannian Newton Trust-Region Method for Fitting Gaussian Mixture Models
• Inference and Mixture Modeling with the Elliptical Gamma Distribution
• Accelerated Stochastic Quasi-Newton Optimization on Riemann Manifolds
• Vector Transport Free Riemannian LBFGS for Optimization on Symmetric Positive Definite Matrix Manifolds