Learning Representation for Multi-View Data Analysis
Synopsis
This book equips readers to handle complex multi-view data representation, centered around several major visual applications, sharing many tips and insights through a unified learning framework. This framework is able to model most existing multi-view learning and domain adaptation, enriching readers’ understanding from their similarity, and differences based on data organization and problem settings, as well as the research goal.
A comprehensive review exhaustively provides the key recent research on multi-view data analysis, i.e., multi-view clustering, multi-view classification, zero-shot learning, and domain adaption. More practical challenges in multi-view data analysis are discussed including incomplete, unbalanced and large-scale multi-view learning. Learning Representation for Multi-View Data Analysis 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.
- Copyright:
- 2019 Springer
Book Details
- Book Quality:
- ISBN-13:
- 9783030007348
- Publisher:
- Springer International Publishing
- Date of Addition:
- 2018-12-08T08:54:15Z
- Language:
- English
- Categories:
- Usage Restrictions:
- This is a copyrighted book.