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UNSUPERVISED CHANGE DETECTION IN SAR IMAGES USING GAUSSIAN MIXTURE MODELS
Gaussian Mixture model Change Detection SAR images Difference image Multi-temporal Images
2016/1/15
In this paper, we propose a method for unsupervised change detection in Remote Sensing Synthetic Aperture Radar (SAR) images. This method is based on the mixture modelling of the histogram of differen...
Application of incremental Gaussian mixture models for characterization of wind field data
Application incremental Gaussian mixture models characterization
2015/6/30
Structural responses and power output of a wind turbine are strongly affected by the wind field acting on the wind turbine. Knowledge about the wind field and its variations is essential not only for ...
Hierarchical Large-Margin Gaussian Mixture Models for Phonetic Classification
hierarchical classifi er committee classi- fi er
2015/3/10
Hierarchical Large-Margin Gaussian Mixture Models for Phonetic Classification.
HIERARCHICAL LARGE-MARGIN GAUSSIAN MIXTURE MODELS FOR PHONETIC CLASSIFICATION
hierarchical classifier committee classifier large margin GMM phonetic classification
2014/11/27
In this paper we present a hierarchical large-margin Gaussian mixture modeling framework and evaluate it on the task of phonetic classification. A two-stage hierarchical classifier is trained by alter...
Quantum Annealing for Dirichlet Process Mixture Models with Applications to Network Clustering
Quantum annealing Dirichlet process Stochastic optimization Maximum a posteriori estimation Bayesian nonparametrics
2013/6/17
We developed a new quantum annealing (QA) algorithm for Dirichlet process mixture (DPM) models based on the Chinese restaurant process (CRP). QA is a parallelized extension of simulated annealing (SA)...
PReMiuM: An R Package for Profile Regression Mixture Models using Dirichlet Processes
Profile regression Clustering Dirichlet process mixture model
2013/4/27
PReMiuM is a recently developed R package for Bayesian clustering using a Dirichlet process mixture model. This model is an alternative to regression models, non-parametrically linking a response vect...
Capturing Patterns via Parsimonious t Mixture Models
Factor analysis Facial representation Image compression PGMM PTMM
2013/4/27
his paper exploits a simplified version of the mixture of multivariate t-factor analyzers (MtFA) for robust mixture modelling and clustering of high-dimensional data that frequently contain a number o...
Bayesian Mixture Models for Frequent Itemset Discovery
Bayesian Mixture Models Frequent Itemset Discovery
2012/11/26
In binary-transaction data-mining, traditional frequent itemset mining often produces results which are not straightforward to interpret. To overcome this problem, probability models are often used to...
Mixture Models for Single Cell Assays with Applications to Vaccine Studies
Mixture Models Single Cell Assays Applications to Vaccine Studies
2012/9/18
Blood and tissue are composed of many functionally distinct cell subsets. In immunological studies, these can only be measured accurately using single-cell assays. The characterization of these small ...
We present the multidimensional membership mixture (M3) models where every dimension of the membership represents an independent mixture model and each
data point is generated from the selected mixtu...
Randomised Mixture Models for Pricing Kernels
Pricing kernel asset pricing, interest rate modelling yield curve randomised mixtures Lévy processes Esscher martingales weighted heat kernel Markov processes
2011/12/28
Numerous kinds of uncertainties may affect an economy, e.g. economic, political, and environmental ones. We model the aggregate impact by the uncertainties on an economy and its associated financial m...
Finite mixture models with predictive recursion marginal likelihood
Density estimation Dirichlet distribution mixture com-plexity
2011/7/6
Estimation of finite mixture models when the mixing distribution support is unknown is an important and challenging problem. In this paper, a new approach is given based on the recently proposed predi...
Semiparametric inference in mixture models with predictive recursion marginal likelihood
Density estimation Dirichlet process mixture empirical Bayes filtering algorithm
2011/7/5
Predictive recursion is an accurate and computationally efficient algorithm for nonparametric estimation of mixing densities in mixture models. In semiparametric mixture models, however, the algorithm...
Constrained Mixture Models for Asset Returns Modelling
return distributions trading strategies Maximisation
2011/3/31
The estimation of asset return distributions is crucial for determining optimal trading strategies. In this paper we describe the constrained mixture model, based on a mixture of Gamma and Gaussian di...
Spades and Mixture Models
Adaptive estimation aggregation lasso minimax risk mixturemodels consistent model selection
2010/3/17
This paper studies sparse density estimation via ℓ1 penalization (SPADES).We
focus on estimation in high-dimensional mixture models and nonparametric adaptive density
estimation. We show, resp...