Amethod is presented for the improvement of the resolution and clarity of bilinear time frequency distributions generated from signals consisting of a number of approximately time-frequency disjoint components. The method involves the determination of the parameters of a finite mixture of Gaussians, which is used to model an initial time-frequency distribution. The expectation-maximisation algo...

Massive transaction data sets are routinely recorded in a variety of applications including telecommunications, retail commerce, and Web site management. In this paper we address the problem of inferring models from such transaction data in the form of predictive profiles of individual behavior. We describe a generative mixture model that accounts for population heterogeneity in transaction gen...

Abstract: The conditional independence assumption for nonparametric multivariate finite mixture models, a weaker form of the well-known conditional independence assumption for random effects models for longitudinal data, is the subject of an increasing number of theoretical and algorithmic developments in the statistical literature. After presenting a survey of this literature, including an in-...

We propose a new method for fitting mixture models that performs component selection and does not require external initialization. The novelty of our approach includes: a minimum message length (MML) type model selection criterion; the inclusion of the criterion into the expectation-maximization (EM) algorithm (which also increases its ability to escape from local maxima); an initialization str...

In this paper, Multi-View Expectation and Maximization algorithm for finite mixture models is proposed by us to handle realworld learning problems which have natural feature splits. Multi-View EM does feature split as Co-training and Co-EM, but it considers multiview learning problems in the EM framework. The proposed algorithm has these impressing advantages comparing with other algorithms in ...

A novel image texture classification method based on finite Gaussian mixture models of sub-band coefficients is proposed in this paper. In the method, an image texture is first decomposed into several sub-bands, then the marginal density distribution of coefficients in each sub-band is approximated by Gaussian mixtures. The Gaussian component parameters are estimated by an “EM+MML” algorithm wh...

This document gives a high-level summary of the necessary details for implementing collapsed Gibbs sampling for fitting Gaussian mixture models (GMMs) following a Bayesian approach. The document structure is as follows. After notation and reference sections (Sections 2 and 3), the case for sampling the parameters of a finite Gaussian mixture model is described in Section 4. This is then extende...

You’ve collected the data and performed a preliminary analysis with a linear regression. But the residuals have several modes, and transformations don’t help. You need a different approach, and that calls for the FMM procedure. PROC FMM fits finite mixture models, which enable you to describe your data with mixtures of different distributions so you can account for underlying heterogeneity and ...

The regularized Mahalanobis distance is proposed in the framework of finite mixture models to avoid commonly faced numerical difficulties encountered with EM. Its principle is applied to Gaussian and Student-t mixtures, resulting in reliable density estimates, the model complexity being kept low. Besides, the regularized models are robust to various noise types. Finally, it is shown that the qu...

The conditional independence assumption for nonparametric multivariate finite mixture models may be considered to be a weaker form of the well-known conditional independence assumption for random effects models for longitudinal data. After summarizing important recent identifiability results, this article describes and extends an algorithm for estimation of the parameters in these models. The a...