Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Floating search methods in feature selection
Pattern Recognition Letters
Generalization of the EM algorithm for mixture density estimation
Pattern Recognition Letters
Mixture Density Estimation Based on Maximum Likelihood and Sequential Test Statistics
Neural Processing Letters
Complexity analysis of RBF networks for Pattern Recognition
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Model Complexity Validation for PDF Estimation Using Gaussian Mixtures
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Modified Predictive Validation Test for Gaussian Mixture Modelling
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Real Time Segmentation of Lip Pixels for Lip Tracker Initialization
CAIP '01 Proceedings of the 9th International Conference on Computer Analysis of Images and Patterns
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Gaussian mixture modelling is used to provide a semi-parametric density description for a given data set. The fundamental problem with this approach is that the number of mixtures required to adequately describe the data is not known in advance. In our previous work [12] we introduced a new concept, termed Predictive Validation as a basis for an automatic method to select the number of components. In this paper we investigate the influence of the various parameters in our model selection method in order to develop it into an operational tool. We also demonstrate the utility of our model validation method to two applications in which the selected models are used for supervised classification and outlier detection tasks.