Advertisement

© 2020

Mixture Models and Applications

  • Nizar Bouguila
  • Wentao Fan

Benefits

  • 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

Book

Part of the Unsupervised and Semi-Supervised Learning book series (UNSESUL)

Table of contents

  1. Front Matter
    Pages i-xii
  2. Gaussian-Based Models

  3. Generalized Gaussian-Based Models

    1. Front Matter
      Pages 59-59
    2. Muhammad Azam, Basim Alghabashi, Nizar Bouguila
      Pages 61-80
    3. Fatma Najar, Sami Bourouis, Rula Al-Azawi, Ali Al-Badi
      Pages 81-106
  4. Spherical and Count Data Clustering

  5. Bounded and Semi-bounded Data Clustering

    1. Front Matter
      Pages 177-177
    2. Kamal Maanicshah, Muhammad Azam, Hieu Nguyen, Nizar Bouguila, Wentao Fan
      Pages 209-233
    3. Meeta Kalra, Michael Osadebey, Nizar Bouguila, Marius Pedersen, Wentao Fan
      Pages 235-269
  6. Image Modeling and Segmentation

    1. Front Matter
      Pages 271-271
    2. Jaspreet Singh Kalsi, Muhammad Azam, Nizar Bouguila
      Pages 273-305
    3. Wenmin Chen, Wentao Fan, Nizar Bouguila, Bineng Zhong
      Pages 307-324
    4. Ines Channoufi, Fatma Najar, Sami Bourouis, Muhammad Azam, Alrence S. Halibas, Roobaea Alroobaea et al.
      Pages 325-348

About this book

Introduction

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.

Keywords

Finite mixture models Infinite mixture models Bayesian/variational learning Nonparametric Bayesian approaches Subspace mixture models Outliers detection High-dimensional data Deep mixture models Unsupervised learning Semi-supervised learning

Editors and affiliations

  • Nizar Bouguila
    • 1
  • Wentao Fan
    • 2
  1. 1.Concordia Institute for Information Systems EngineeringConcordia UniversityMontrealCanada
  2. 2.Department of Computer Science and TechnologyHuaqiao UniversityXiamenChina

About the editors

Nizar Bouguila received the engineer degree from the University of Tunis, Tunis, Tunisia, in 2000, and the M.Sc. and Ph.D. degrees in computer science from Sherbrooke University, Sherbrooke, QC, Canada, in 2002 and 2006, respectively. He is currently a Professor with the Concordia Institute for Information Systems Engineering (CIISE) at Concordia University, Montreal, Quebec, Canada. His research interests include image processing, machine learning, data mining, computer vision, and pattern recognition. Prof. Bouguila received the best Ph.D Thesis Award in Engineering and Natural Sciences from Sherbrooke University in 2007. He was awarded the prestigious Prix d’excellence de l’association des doyens des etudes superieures au Quebec (best Ph.D Thesis Award in Engineering and Natural Sciences in Quebec), and was a runner-up for the prestigious NSERC doctoral prize. He is the author or co-author of more than 200 publications in several prestigious journals and conferences. He is a regular reviewer for many international journals and serving as associate editor for several journals such as Pattern Recognition. Dr. Bouguila is a licensed Professional Engineer registered in Ontario, and a Senior Member of the IEEE. He is the holder of the Concordia University Research Chair.

Wentao Fan received his M.Sc. and Ph.D. degrees in electrical and computer engineering from Concordia University, Montreal, Quebec, Canada, in 2009 and 2014, respectively. He is currently an Associate Professor in the Department of Computer Science and Technology, Huaqiao University, Xiamen, China. His research interests include machine learning, computer vision, deep learning and pattern recognition.

Bibliographic information

Industry Sectors
Pharma
Automotive
Biotechnology
Electronics
IT & Software
Telecommunications
Law
Aerospace
Oil, Gas & Geosciences
Engineering

Reviews

“This book can be taken as a review of the subject. It is also a very good starting point for understanding mixture modeling and even for setting up new research. I strongly recommend this work for researchers and advanced undergraduate and graduate students of computer science and applied probability.” (Arturo Ortiz-Tapia, Computing Reviews, January 18, 2021)

“[T]his … is … a very good starting point for understanding mixture modeling and even for setting up new research. I strongly recommend this work for researchers and advanced undergraduate and graduate students of computer science and applied probability.” (Computing Reviews)