This paper presents a probabilistic approach to analyze kernel principal components by naturally combining in one treatment the theory of probabilistic principal component analysis and that of kernel principal component analysis. In this formulation, the kernel component enhances the nonlinear modeling power, while the probabilistic structure offers (i) a mixture model for nonlinear data struct...

An important but difficult problem of mixture model is estimating the number of components, k, by model selection criterion. We investigate the sum of weighted real and imaginary parts of all LogCharacteristic Functions (LCF) for Gaussian Mixture Model (GMM) and propose a new method to estimate k, adaptively. Our method defines the Sum of Weighted Real parts of all LCFs (SWRLCF) as a new conver...

Measured physical spectra often comprise an unknown number of components of known parametric family. A reversible jump Markov chain Monte Carlo (RJMCMC) technique is applied to the problem of estimating the number of components evident in the data jointly with the parameters of the components. The physical model consists of a mixture of components, an additive background, and a convolution with...

This article discusses the problem of estimation of parameters in finite mixtures when the mixture components are assumed to be symmetric and to come from the same location family. We refer to these mixtures as semi-parametric because no additional assumptions other than symmetry are made regarding the parametric form of the component distributions. Because the class of symmetric distributions ...

Binary mixtures of taste stimuli were applied to the tongue of the hamster and the reaction of the whole corda tympani was recorded. Some of the chemicals that were paired in mixtures (HCl, NH4Cl, NaCl, CaCl2, sucrose, and D-phenylalanine) have similar tastes to human and/or hamster, and/or common stimulatory effects on individual fibers of the hamster chorda tympani; other pairs of these chemi...

the fisher information matrix for a mixture of two pareto distributions is derived. numericaltabulations of the matrix are also provided for practical purposes. the work is motivated by a real-lifeexample.

Variational Learning for Finite Inverted Dirichlet Mixture Models and Its Applications Parisa Tirdad Clustering is an important step in data mining, machine learning, computer vision and image processing. It is the process of assigning similar objects to the same subset. Among available clustering techniques, finite mixture models have been remarkably used, since they have the ability to consid...

A new method is proposed for selection of the optimal number of components of a mixture model for pattern classiication. We approximate a class-conditional density by a mixture of Gaussian components. We estimate the parameters of the mixture components by the EM (Expectation Maximization) algorithm and select the optimal number of components on the basis of the MDL (Minimum Description Length)...

This article discusses the problem of estimation of parameters in finite mixtures when the mixture components are assumed to be symmetric and to come from the same location family. We refer to these mixtures as semiparametric because no additional assumptions other than symmetry are made regarding the parametric form of the component distributions. Because the class of symmetric distributions i...