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Recent Advancements in Multi-View Data Analytics

  • Book
  • © 2022

Overview

Editors:
  1. Witold Pedrycz

    1. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada

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  2. Shyi-Ming Chen

    1. Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

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    You can also search for this editor in PubMed   Google Scholar

  • Is self-contained and easily accessible to the broad research community
  • Offers some introductory chapters on multi-view data analytics
  • Starts with fundamentals, moves on to methodological issues, afterward concentrates on representative algorithms

Part of the book series: Studies in Big Data (SBD, volume 106)

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Table of contents (12 chapters)

  1. Front Matter

    Pages i-viii
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  2. The Psychology of Conflictive Uncertainty

    • Michael Smithson
    Pages 1-21

  3. How Multi-view Techniques Can Help in Processing Uncertainty

    • Olga Kosheleva, Vladik Kreinovich
    Pages 23-53

  4. Multi-view Clustering and Multi-view Models

    • Nha Pham Van, Long Ngo Thanh, Long Pham The
    Pages 55-96

  5. Rethinking Collaborative Clustering: A Practical and Theoretical Study Within the Realm of Multi-view Clustering

    • Pierre-Alexandre Murena, Jérémie Sublime, Basarab Matei
    Pages 97-130

  6. An Optimal Transport Framework for Collaborative Multi-view Clustering

    • Fatima-Ezzahraa Ben-Bouazza, Younès Bennani, Mourad El Hamri
    Pages 131-157

  7. Data Anonymization Through Multi-modular Clustering

    • Nicoleta Rogovschi, Younès Bennani, Sarah Zouinina
    Pages 159-176

  8. Multi-view Clustering Based on Non-negative Matrix Factorization

    • Nistor Grozavu, Basarab Matei, Younès Bennani, Kaoutar Benlamine
    Pages 177-200

  9. A Graph-Based Multi-view Clustering Approach for Continuous Pattern Mining

    • Christoffer Åleskog, Vishnu Manasa Devagiri, Veselka Boeva
    Pages 201-237

  10. Learning Shared and Discriminative Information from Multiview Data

    • Jia Chen, Hongjie Cao, Alireza Sadeghi, Gang Wang
    Pages 239-268

  11. A Supervised Ensemble Subspace Learning Model Based on Multi-view Feature Fusion Employing Multi-template EMG Signals

    • Aditya Saikia, Anil Hazarika, Bikram Patir, Amarprit Singh
    Pages 269-288

  12. Performance Profiling of Operating Modes via Multi-view Analysis Using Non-negative Matrix Factorisation

    • Michiel Dhont, Elena Tsiporkova, Veselka Boeva
    Pages 289-316

  13. A Methodology Review on Multi-view Pedestrian Detection

    • Rui Qiu, Ming Xu, Yuyao Yan, Jeremy S. Smith
    Pages 317-339

  14. Back Matter

    Pages 341-342
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Keywords

About this book

This book provides timely studies on multi-view facets of data analytics by covering recent trends in processing and reasoning about data originating from an array of local sources. A multi-view nature of data analytics is encountered when working with a variety of real-world scenarios including clustering, consensus building in decision processes, computer vision, knowledge representation, big data, data streaming, among others.

The chapters demonstrate recent pursuits in the methodology, theory, advanced algorithms, and applications of multi-view data analytics and bring new perspectives of data interpretation. The timely book will appeal to a broad readership including both researchers and practitioners interested in gaining exposure to the rapidly growing trend of multi-view data analytics and intelligent systems.

Editors and Affiliations

  • Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada

    Witold Pedrycz

  • Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

    Shyi-Ming Chen

Bibliographic Information

  • Book Title: Recent Advancements in Multi-View Data Analytics

  • Editors: Witold Pedrycz, Shyi-Ming Chen

  • Series Title: Studies in Big Data

  • DOI: https://doi.org/10.1007/978-3-030-95239-6

  • Publisher: Springer Cham

  • eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022

  • Hardcover ISBN: 978-3-030-95238-9Published: 21 May 2022

  • Softcover ISBN: 978-3-030-95241-9Published: 21 May 2023

  • eBook ISBN: 978-3-030-95239-6Published: 20 May 2022

  • Series ISSN: 2197-6503

  • Series E-ISSN: 2197-6511

  • Edition Number: 1

  • Number of Pages: VIII, 342

  • Number of Illustrations: 27 b/w illustrations, 47 illustrations in colour

  • Topics: Data Engineering, Computational Intelligence, Artificial Intelligence

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