Infinite generalized gaussian mixture modeling and applications

作者: Tarek Elguebaly , Nizar Bouguila

DOI: 10.1007/978-3-642-21593-3_21

关键词: OverfittingArtificial intelligenceFacial recognition systemMachine learningApplied mathematicsMixture modelingMathematicsBayesian probabilityGaussianDifficult problemMixture modelInfrared face recognition

摘要: A fully Bayesian approach to analyze infinite multidimensional generalized Gaussian mixture models (IGGM) is developed in this paper. The framework used avoid model overfitting and the assumption adopted difficult problem of finding right number components. utility proposed demonstrated by applying it on texture classification infrared face recognition, while comparing different other approaches.

参考文章(2)
George Casella, Christian P. Robert, Monte Carlo Statistical Methods (Springer Texts in Statistics) Springer-Verlag New York, Inc.. ,(2005)
George Casella, Christian P. Robert, Monte Carlo Statistical Methods ,(1999)