Robust Representation for Data Analytics: Models and Applications - Couverture rigide

Li, Sheng; Fu, Yun

Couverture rigide

ISBN 10 : 331960175X ISBN 13 : 9783319601755

Editeur : Springer International Publishing AG, 2017

Synopsis

This book introduces the concepts and models of robust representation learning, and provides a set of solutions to deal with real-world data analytics tasks, such as clustering, classification, time series modeling, outlier detection, collaborative filtering, community detection, etc. Three types of robust feature representations are developed, which extend the understanding of graph, subspace, and dictionary.

Leveraging the theory of low-rank and sparse modeling, the authors develop robust feature representations under various learning paradigms, including unsupervised learning, supervised learning, semi-supervised learning, multi-view learning, transfer learning, and deep learning. Robust Representations for Data Analytics covers a wide range of applications in the research fields of big data, human-centered computing, pattern recognition, digital marketing, web mining, and computer vision.

A propos du livre

À propos de l'auteur

SHEN Yang is an assistant research fellow in the Collaborative Innovation Center of Assessment for Basic Education Quality, Beijing Normal University, China. She received a Bachelor of Science (B.Sc.), a Master of Science (M.Sc.), and a Doctor of Philosophy (Ph.D.) degree in Northwestern Polytechnical University, China. From 2019-2021, she worked in the National Engineering Research Center of Cyberlearning and Intelligent Technology as a postdoctoral research fellow and was on secondment to the Ministry of Education (MOE), China. Her research interests include big data and artificial intelligent application in education, psychological evaluation of human-computer interaction, evaluation on virtual reality learning environment, artificial intelligence/virtual reality/augmented reality application and strategies in education assessment. YIN Xin is the technical director of the Technology Transfer Center of Capital Normal University, China, and a part-time researcher of the National Engineering Research Center of Cyberlearning and Intelligent Technology. His research interests are intelligent education, transformation of scientific and technological achievements, and intellectual property management. JIANG Yu is an associate researcher of the Center for Educational Technology and Resource Development, Ministry of Education, P.R. China(National Center for Educational Technology) , and the deputy secretary-general of the Online Education Committee of the China Federation of Internet Societies. His main research interests are online education and learning science. KONG Lingkai is the deputy general manager of the Education Product Center of China Mobile (Chengdu, China) Research Institute. His main research field is collaborative development and innovation of Information and Communication Technology (ICT) and education informatization.

LI Sheng is a senior engineerand a leading professional of China Mobile (Chengdu, China) Research Institute, a director of the Education Industry, a co-director of the Telecom-education Interactive Laboratory of the National Engineering Research Center of Cyberlearning and Intelligent Technology, and a director of the Ubiquitous Learning Center of the Joint Laboratory for Mobile Learning, at the Ministry of Education - China Mobile Communications Corporation. His research interests include mobile education and ubiquitous learning and key technologies of online learning platforms.

ZENG Haijun, Ph.D., is the vice dean of the Smart Learning Institute of Beijing Normal University, China, the deputy director of the Educational Informatization Strategy Research Base (Beijing, China) of the Ministry of Education, and the office director of the National Engineering Research Center of Cyberlearning and Intelligent Technology. His main research interests are online education and digital learning support (DLS) service system.

Leveraging the theory of low-rank and sparse modeling, the authors develop robust feature representations under various learning paradigms, including unsupervised learning, supervised learning, semi-supervised learning, multi-view learning, transfer learning, and deep learning. Robust Representations for Data Analytics covers a wide range of applications in the research fields of big data, human-centered computing, pattern recognition, digital marketing, web mining, and computer vision.

Détails de l'édition

• Éditeur: Springer International Publishing AG
• Date d'édition: 2017
• Langue: anglais
• ISBN 10: 331960175X
• ISBN 13: 9783319601755
• Reliure: Relié
• Numéro d'édition: 1
• Nombre de pages: 224
• Coordonnées du fabricant: Springer Nature Customer Service Center GmbH; ProductSafety@springernature.com; ; Europaplatz 3; Heidelberg; 69115; Allemagne