Results 271 to 280 of about 901,615 (325)

Parsimonious Gaussian mixture models

Statistics and Computing, 2008
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.
Paul David McNicholas, Thomas Brendan Murphy   +1 more
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Feature-guided Gaussian mixture model for image matching

Pattern Recognition, 2019
This study proposes a novel feature-guided Gaussian mixture model (FG-GMM) for image matching, which generally requires matching two sets of feature points extracted from the provided images.
Jiayi Ma, Xingyu Jiang, Junjun Jiang, Yuan Gao   +3 more
semanticscholar   +1 more source

Gaussian Mixture Models

2014
In this chapter we first introduce the basic concepts of random variables and the associated distributions. These concepts are then applied to Gaussian random variables and mixture-of-Gaussian random variables. Both scalar and vector-valued cases are discussed and the probability density functions for these random variables are given with their ...
Dong Yu, Li Deng
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Secure State Estimation Against Integrity Attacks: A Gaussian Mixture Model Approach

IEEE Transactions on Signal Processing, 2019
We consider the problem of estimating the state of a linear time-invariant Gaussian system using $N$ sensors, where a subset of the sensors can potentially be compromised by an adversary.
Ziyang Guo, Dawei Shi, D. Quevedo, Ling Shi   +3 more
semanticscholar   +1 more source

Hierarchical Gaussian mixture model

2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010
Gaussian mixture models (GMMs) are a convenient and essential tool for the estimation of probability density functions. Although GMMs are used in many research domains from image processing to machine learning, this statistical mixture modeling is usually com- plex and further needs to be simplified.
Vincent Garcia, Frank Nielsen, Richard Nock   +2 more
openaire   +1 more source

Identification of typical building daily electricity usage profiles using Gaussian mixture model-based clustering and hierarchical clustering

Applied Energy, 2018
This paper presents a clustering-based strategy to identify typical daily electricity usage (TDEU) profiles of multiple buildings. Different from the majority of existing clustering strategies, the proposed strategy consists of two levels of clustering ...
Kehua Li, Zhenjun Ma, D. Robinson, Jun Ma   +3 more
semanticscholar   +1 more source

Automatic Visual Detection System of Railway Surface Defects With Curvature Filter and Improved Gaussian Mixture Model

IEEE Transactions on Instrumentation and Measurement, 2018
Rails are among the most important components of railway transportation, and real-time defects detection of the railway is an important and challenging task because of intensity inhomogeneity, low contrast, and noise.
Hui Zhang, Xiating Jin, Q. M. J. Wu, Yaonan Wang, Zhendong He, Yimin Yang   +5 more
semanticscholar   +1 more source

A Novel Gaussian Mixture Model for Classification

IEEE International Conference on Systems, Man and Cybernetics, 2019
Gaussian Mixture Model (GMM) is a probabilistic model for representing normally distributed subpopulations within an overall population. It is usually used for unsupervised learning to learn the subpopulations and the subpopulation assignment ...
Huan Wan, Hui Wang, B. Scotney, Jun Liu
semanticscholar   +1 more source

Edgeworth-Expanded Gaussian Mixture Density Modeling

Neural Computation, 2005
Instead of increasing the order of the Edgeworth expansion of a single gaussian kernel, we suggest using mixtures of Edgeworth-expanded gaussian kernels of moderate order. We introduce a simple closed-form solution for estimating the kernel parameters based on weighted moment matching.
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Gaussian process modelling with Gaussian mixture likelihood

Journal of Process Control, 2019
Abstract Gaussian Process (GP), as a probabilistic non-linear multi-variable regression model, has been widely used in nonparametric Bayesian framework for the data based modelling of complex processes. The noise dynamics in standard GP regression is assumed to follow a Gaussian distribution.
Atefeh Daemi, Hariprasad Kodamana, Biao Huang   +2 more
openaire   +1 more source