Learning Representation for Multi View Data Analysis: Models and Applications By: Zhengming Ding, Handong Zhao, Yun Fu

Author: Zhengming Ding
Publisher: Springer; 1st ed. 2019 edition (December 6, 2018)
Description:

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.

3.0M	Learning Representation for Multi-View Data Analysis_ Models and Applications - Ding, Fu, Zhao.pdf
3.0M	total