We introduce a new method for data clustering based on a particular Gaussian mixture model (GMM). Each cluster of data, modeled as a GMM into an input space, is interpreted as a hyperplane in a high dimensional mapping space where the underlying coefficients are found by solving a quadratic programming (QP) problem. The main contributions of this work are (1) an original probabilistic framework...

Our approach to the ImageCLEF 2010 tasks is based on Histogram of Oriented Gradients descriptors (HOG) and Okapi BM25 based text retrieval. We extracted feature vectors to describe the visual content of an image region or the entire image. We trained a Gaussian Mixture Model (GMM) to cluster the feature vectors extracted from the image regions. To represent each image with only one vector we co...

Our recent experiments with Gaussian mixture (GMM) based acoustic models have shown that employing context dependent acoustic models, namely triphones, can greatly improve recognition accuracy [1] in comparison to systems based on context independent units. Significant portion of our research has been aimed at exploring the possibilities of neural networks as acoustic models for speech recognit...

In this paper, we proposed an unsupervised posture modeling method based on Gaussian Mixture Model (GMM). Specifically, each learning posture is described based on its movement features by a set of spatial-temporal interest points (STIPs), salient postures are then clustered from these training samples by an unsupervised algorithm, here we give the comparison of four candidate classification me...

In order to cluster or partition data, we often use Expectation-and-Maximization (EM) or Variational approximation with a Gaussian Mixture Model (GMM), which is a parametric probability density function represented as a weighted sum of K̂ Gaussian component densities. However, model selection to find underlying K̂ is one of the key concerns in GMM clustering, since we can obtain the desired clust...

Urban traffic forecasting models generally follow either a Gaussian Mixture Model (GMM) or Support Vector Classifier (SVC) to estimate the features of potential road accidents. Although SVC can provide good performances with less data than GMM, it incurs higher computational cost. This paper proposes novel framework that combines descriptive strength high-performance classification capabilities...

Boosting is an effecient method to improve the classification performance. Recent theoretical work has shown that the boosting technique can be viewed as a gradient descent search for a good fit in function space. Several authors have applied such viewpoint to solve the density estimation problems. In this paper we generalize such framework to a specific density model – Gaussian Mixture Model (...

A hybrid Hidden Markov Model (HMM) Gaussian Mixture Model (GMM) system was proposed to automatically select tokens of /iau/, /ai/, /ei/, /m/ and /n/ in a database of recordings of Standard-Chinese speech collected under studio-clean, mobile-landline degraded and mismatched recording conditions. The FVC systems constructed were all MFCC GMM-UBM systems, but based on different portions of the rec...

This paper discusses a method for performing independent component analysis exploiting Gaussian mixture models (GMMs). Previously most techniques that combine these methods have used GMMs to model the source signals. This paper considers a parsimonious method for modelling the observed signals. The GMM is fitted to the observed data using a modified version of the expectation maximisation algor...

Identification of operating regime based models of nonlinear dynamic systems is addressed. The operating regimes and the parameters of the local linear models are identified directly and simultaneously based on the Expectation Maximization (EM) identification of Gaussian Mixture Model (GMM). The proposed technique is demonstrated by means of the identification of a neutralization reaction in a ...