scholarly journals Gaussian mixture models for the classification of high-dimensional vibrational spectroscopy data

2010 ◽  
Vol 24 (11-12) ◽  
pp. 719-727 ◽  
Author(s):  
Julien Jacques ◽  
Charles Bouveyron ◽  
Stéphane Girard ◽  
Olivier Devos ◽  
Ludovic Duponchel ◽  
...  
Keyword(s):  
Mixture Models ◽  
Vibrational Spectroscopy ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
High Dimensional ◽  
Spectroscopy Data

Classification of primary radar tracks using Gaussian mixture models

2009 ◽  
Vol 3 (6) ◽  
pp. 559
Author(s):  
L.P. Espindle ◽  
M.J. Kochenderfer
Keyword(s):  
Mixture Models ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture

scholarly journals Regularized Parameter Estimation in High-Dimensional Gaussian Mixture Models

2011 ◽  
Vol 23 (6) ◽  
pp. 1605-1622 ◽  
Author(s):  
Lingyan Ruan ◽  
Ming Yuan ◽  
Hui Zou
Keyword(s):  
Parameter Estimation ◽  
Mixture Models ◽  
Gaussian Mixture Models ◽  
Expectation Maximization Algorithm ◽  
Real Data ◽  
Gaussian Mixture ◽  
High Dimensional ◽  
Model Based Clustering ◽  
Text Type ◽  
Effective Dimensionality

Finite gaussian mixture models are widely used in statistics thanks to their great flexibility. However, parameter estimation for gaussian mixture models with high dimensionality can be challenging because of the large number of parameters that need to be estimated. In this letter, we propose a penalized likelihood estimator to address this difficulty. The [Formula: see text]-type penalty we impose on the inverse covariance matrices encourages sparsity on its entries and therefore helps to reduce the effective dimensionality of the problem. We show that the proposed estimate can be efficiently computed using an expectation-maximization algorithm. To illustrate the practical merits of the proposed method, we consider its applications in model-based clustering and mixture discriminant analysis. Numerical experiments with both simulated and real data show that the new method is a valuable tool for high-dimensional data analysis.

Classification of a known sequence of motions and postures from accelerometry data using adapted Gaussian mixture models

2006 ◽  
Vol 27 (10) ◽  
pp. 935-951 ◽  
Author(s):  
Felicity R Allen ◽  
Eliathamby Ambikairajah ◽  
Nigel H Lovell ◽  
Branko G Celler
Keyword(s):  
Mixture Models ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture

scholarly journals New Families in our Solar Neighborhood: Applying Gaussian Mixture Models for Objective Classification of Structures in the Milky Way and in Simulations

2021 ◽  
Vol 921 (2) ◽  
pp. 106
Author(s):  
Farnik Nikakhtar ◽  
Robyn E. Sanderson ◽  
Andrew Wetzel ◽  
Sarah Loebman ◽  
Sanjib Sharma ◽  
...  
Keyword(s):  
Mixture Models ◽  
Milky Way ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Solar Neighborhood ◽  
Objective Classification
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