An Adapted Gaussian Mixture Model Approach to Accelerometry-Based Movement Classification Using Time-Domain Features

2006 ◽  
Author(s):  
Felicity R. Allen ◽  
Eliathamby Ambikairajah ◽  
Nigel H. Lovell ◽  
Branko G. Celler
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Time Domain ◽  
Gaussian Mixture ◽  
Mixture Model Approach ◽  
Model Approach ◽  
Movement Classification

A Gaussian Mixture Model Approach to Forecast Verification

2010 ◽  
Vol 25 (3) ◽  
pp. 908-920 ◽  
Author(s):  
Valliappa Lakshmanan ◽  
John S. Kain
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Model Fit ◽  
Forecast Verification ◽  
New Approach ◽  
Mixture Model Approach ◽  
Object Based ◽  
Verification Methods ◽  
Model Approach

Abstract Verification methods for high-resolution forecasts have been based either on filtering or on objects created by thresholding the images. The filtering methods do not easily permit the use of deformation while identifying objects based on thresholds can be problematic. In this paper, a new approach is introduced in which the observed and forecast fields are broken down into a mixture of Gaussians, and the parameters of the Gaussian mixture model fit are examined to identify translation, rotation, and scaling errors. The advantages of this method are discussed in terms of the traditional filtering or object-based methods and the resulting scores are interpreted on a standard verification dataset.

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