Title :
PyMEF — A framework for exponential families in Python
Author :
Schwander, Olivier ; Nielsen, Frank
Author_Institution :
Ecole Polytech., Palaiseau, France
Abstract :
Modeling data is often a critical step in many challenging applications in computer vision, bioinformatics or machine learning. Gaussian Mixture Models are a popular choice in many applications. Although these mixtures are powerful enough to approximate complex distributions, they may not be the best choice for some applications. Usual software mixtures libraries are often limited to a particular kind of distribution, which makes difficult to change the distribution and so to choose the best one. In this paper we focus on a particular class of distributions, the exponential families (which contains a lot of usual distributions like Gaussian, Rayleigh or Gamma). We present pyMEF, a Python framework to manipulate, learn and simplify mixtures of exponential families.
Keywords :
Gaussian processes; Gaussian mixture models; PyMEF; Python; bioinformatics; computer vision; data modeling; exponential families; machine learning; software mixtures libraries; Clustering algorithms; Computational modeling; Data models; Gaussian distribution; Libraries; Probability density function; Software; Clustering; Expectation-Maximization; Exponential family; Gaussian; Mixture model;
Conference_Titel :
Statistical Signal Processing Workshop (SSP), 2011 IEEE
Conference_Location :
Nice
Print_ISBN :
978-1-4577-0569-4
DOI :
10.1109/SSP.2011.5967790