Number of Components and Initialization in Gaussian Mixture Model for Pattern Recognition
Number of components and initial parameter estimates are of crucial importance for sueeessful mixture estimation using Expectation-Maximization (EM) algorithm. In the paper a method for the complete mixture initialization based on a product kernel estimate of probability density function is proposed. The mixture components are assumed here to correspond to local maxima of optimally smoothed kerne Idensity estimate. The gradient method is used for local extrema finding. Then, local extrema are grouped together to form component eandidates and these are merged by the 4hierarchical clustering method. Finally, the initial mixture parameters are estimated. A comparison to scale-space approaches for finding of the number of components is given on examples.
KeywordsMixture Model Gaussian Mixture Model Local Extremum Finite Mixture Model Pattern Recognition Letter
Unable to display preview. Download preview PDF.
- J. Grim, J. Novovicova, P. Pudil, P. Somol, and F. Ferri. Initialization normal mixtures of densities. In Proceedings of the 14th ICPR, pages 886–890, Australia, 1998.Google Scholar
- H. Tenmoto, M. Kudo, and M. Shimbo. MDL-Based Selection of the Number of Components in Mixture Models for Pattern Recognition. In Lecture Notes in Computer Science 1451: Advances in Pattern Recognition, pages 831–836, 1998.Google Scholar