Mixture Models and Applications

Description

This book focuses on recent advances, approaches, theories and applications related to mixture models. In particular, it presents recent unsupervised and semi-supervised frameworks that consider mixture models as their main tool. The chapters considers mixture models involving several interesting and challenging problems such as parameters estimation, model selection, feature selection, etc. The goal of this book is to summarize the recent advances and modern approaches related to these problems. Each contributor presents novel research, a practical study, or novel applications based on mixture models, or a survey of the literature. Reports advances on classic problems in mixture modeling such as parameter estimation, model selection, and feature selection;Present theoretical and practical developments in mixture-based modeling and their importance in different applications;Discusses perspectives and challenging future works related to mixture modeling. Front Matter ....Pages i-xii Front Matter ....Pages 1-1 A Gaussian Mixture Model Approach to Classifying Response Types (Owen E. Parsons)....Pages 3-22 Interactive Generation of Calligraphic Trajectories from Gaussian Mixtures (Daniel Berio, Frederic Fol Leymarie, Sylvain Calinon)....Pages 23-38 Mixture Models for the Analysis, Edition, and Synthesis of Continuous Time Series (Sylvain Calinon)....Pages 39-57 Front Matter ....Pages 59-59 Multivariate Bounded Asymmetric Gaussian Mixture Model (Muhammad Azam, Basim Alghabashi, Nizar Bouguila)....Pages 61-80 Online Recognition via a Finite Mixture of Multivariate Generalized Gaussian Distributions (Fatma Najar, Sami Bourouis, Rula Al-Azawi, Ali Al-Badi)....Pages 81-106 Front Matter ....Pages 107-107 L2 Normalized Data Clustering Through the Dirichlet Process Mixture Model of von Mises Distributions with Localized Feature Selection (Wentao Fan, Nizar Bouguila, Yewang Chen, Ziyi Chen)....Pages 109-123 Deriving Probabilistic SVM Kernels from Exponential Family Approximations to Multivariate Distributions for Count Data (Nuha Zamzami, Nizar Bouguila)....Pages 125-153 Toward an Efficient Computation of Log-Likelihood Functions in Statistical Inference: Overdispersed Count Data Clustering (Masoud Daghyani, Nuha Zamzami, Nizar Bouguila)....Pages 155-176 Front Matter ....Pages 177-177 A Frequentist Inference Method Based on Finite Bivariate and Multivariate Beta Mixture Models (Narges Manouchehri, Nizar Bouguila)....Pages 179-208 Finite Inverted Beta-Liouville Mixture Models with Variational Component Splitting (Kamal Maanicshah, Muhammad Azam, Hieu Nguyen, Nizar Bouguila, Wentao Fan)....Pages 209-233 Online Variational Learning for Medical Image Data Clustering (Meeta Kalra, Michael Osadebey, Nizar Bouguila, Marius Pedersen, Wentao Fan)....Pages 235-269 Front Matter ....Pages 271-271 Color Image Segmentation Using Semi-bounded Finite Mixture Models by Incorporating Mean Templates (Jaspreet Singh Kalsi, Muhammad Azam, Nizar Bouguila)....Pages 273-305 Medical Image Segmentation Based on Spatially Constrained Inverted Beta-Liouville Mixture Models (Wenmin Chen, Wentao Fan, Nizar Bouguila, Bineng Zhong)....Pages 307-324 Flexible Statistical Learning Model for Unsupervised Image Modeling and Segmentation (Ines Channoufi, Fatma Najar, Sami Bourouis, Muhammad Azam, Alrence S. Halibas, Roobaea Alroobaea et al.)....Pages 325-348 Back Matter ....Pages 349-355