in this paper, we try to find a particular combination of wavelet shrinkage and nonlinear diffusion for noise removal in dental images. we selected the wavelet diffusion and modified its automatic threshold selection by proposing new models for speckle related modulus. the laplacian mixture model and circular symmetric laplacian mixture models were evaluated and as it could be expected, the lat...

Consider the dataset of height and weight in Figure 1. It is clear that there are two subpopulations in this data set, and in this case they are easy to interpret: one represents males and the other females. Within each class or cluster, the data is fairly well represented by a 2D Gaussian (as can be seen from the fitted ellipses), but to model the data as a whole, we need to use a mixture of G...

This note is completely expository, and contains a whirlwind abridged introduction to the topic of mixture models by focusing on the application of clustering. More detailed and complete expositions are available in the literature; for instance, the standard machine learning texts (e.g. [Mur12, Bis06]) provide a thorough treatment of this material.

We present an expectation-maximization (EM) algorithm that yields topology preserving maps of data based on probabilistic mixture models. Our approach is applicable to any mixture model for which we have a normal EM algorithm. Compared to other mixture model approaches to self-organizing maps, the function our algorithm maximizes has a clear interpretation: it sums data log-likelihood and a pen...

We consider the problem of learning density mixture models for Classification. Traditional learning of mixtures for density estimation focuses on models that correctly represent the density at all points in the sample space. Discriminative learning, on the other hand, aims at representing the density at the decision boundary. We introduce novel discriminative learning methods for mixtures of ge...

In this paper, in order to improve both the performance and the efficiency of the conventional Gaussian Mixture Models (GMMs), generalized GMMs are firstly introduced by integrating the conventional GMMs and the active curve axis GMMs for fitting non-linear datasets, and then two types of Fuzzy Gaussian Mixture Models (FGMMs) with a faster convergence process are proposed based on the generaliz...

We propose a more efficient version of the slice sampler for Dirichlet process mixture models described by Walker (Commun. Stat., Simul. Comput. 36:45–54, 2007). This new sampler allows for the fitting of infinite mixture models with a wide-range of prior specifications. To illustrate this flexibility we consider priors defined through infinite sequences of independent positive random variables...

Deep learning is a hierarchical inference method formed by subsequent multiple layers of learning able to more efficiently describe complex relationships. In this work, Deep Gaussian Mixture Models are introduced and discussed. A Deep Gaussian Mixture model (DGMM) is a network of multiple layers of latent variables, where, at each layer, the variables follow a mixture of Gaussian distributions....

Parsimonious Gaussian mixture models are developed using a latent Gaussian model which is closely related to the factor analysis model. These models provide a unified modeling framework which includes the mixtures of probabilistic principal component analyzers and mixtures of factor of analyzers models as special cases. In particular, a class of eight parsimonious Gaussian mixture models which ...