Results 231 to 240 of about 102,586 (261)

Splitting Gaussians in Mixture Models

2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance, 2012
Gaussian mixture models have been extensively used and enhanced in the surveillance domain because of their ability to adaptively describe multimodal distributions in real-time with low memory requirements. Nevertheless, they still often suffer from the problem of converging to poor solutions if the main mode stretches and thus over-dominates weaker ...
Rubén Heras Evangelio, Michael Pätzold, Thomas Sikora   +2 more
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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
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Gaussian mixture linear prediction

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014
This work introduces an approach to linear predictive signal analysis utilizing a Gaussian mixture autoregressive model. By initializing different autoregressive states of the model to approximately correspond to the target signal and the expected type of undesired signal components, such as background noise, the iterative parameter estimation ...
Jouni Pohjalainen, Paavo Alku
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Learning mixtures of arbitrary gaussians

Proceedings of the thirty-third annual ACM symposium on Theory of computing, 2001
Mixtures of gaussian (or normal) distributions arise in a variety of application areas. Many techniques have been proposed for the task of finding the component gaussians given samples from the mixture, such as the EM algorithm, a local-search heuristic from Dempster, Laird and Rubin~(1977).
Sanjeev Arora, Ravi Kannan
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Combining Gaussian Mixture Models

2004
A Gaussian mixture model (GMM) estimates a probability density function using the expectation-maximization algorithm. However, it may lead to a poor performance or inconsistency. This paper analytically shows that performance of a GMM can be improved in terms of Kullback-Leibler divergence with a committee of GMMs with different initial parameters ...
Hyoungjoo Lee, Sungzoon Cho
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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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Modelling profiles with a mixture of Gaussians

Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101), 2002
Point distribution models are useful tools for modelling the variability of particular classes of shapes. A common approach is to apply a principle component analysis to the data, to reduce the dimensionality of the representation. However, a single multivariate Gaussian model of the probability density, estimated from the principle covariances, can be
James Orwell, Darrel Greenhill, Jonathan D. Rymel, Graeme A. Jones   +3 more
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Box Gaussian Mixture Filter $ $

IEEE Transactions on Automatic Control, 2010
This note presents the box Gaussian mixture filter (BGMF), which is an efficient filter for the systems with mainly linear measurements but enables utilizing highly nonlinear measurements. BGMF contains a new way to approximate the prior distributions with a Gaussian mixture, whose components have small covariances.
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Density Boosting for Gaussian Mixtures

2004
Ensemble method is one of the most important recent developments in supervised learning domain. Performance advantage has been demonstrated on problems from a wide variety of applications. By contrast, efforts to apply ensemble method to unsupervised domain have been relatively limited.
Xubo B. Song, Kun Yang, Misha Pavel
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The Infinite Gaussian Mixture Model.

2000
In a Bayesian mixture model it is not necessary a priori to limit the number of components to be finite. In this paper an infinite Gaussian mixture model is presented which neatly sidesteps the difficult problem of finding the ``right'' number of mixture components.
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