Multi-view learning overview: Recent progress and new challenges

• Published in: Information fusion 2017-11, Vol.38, p.43-54
• Main Authors: Zhao, Jing, Xie, Xijiong, Xu, Xin, Sun, Shiliang
• Format: Article
• Language: English
• Subjects: Co-regularization; Co-training; Margin consistency; Multi-view learning; Statistical learning theory
• ISSN: 1566-2535; 1872-6305
• DOI: 10.1016/j.inffus.2017.02.007

•This overview reviews theoretical underpinnings of multi-view learning.•It provides comprehensive introduction for the recent developments of multi-view learning methods.•It attempts to identify promising venues and point out some specific challenges. Multi-view learning is an emerging direction in machine learning which considers learning with multiple views to improve the generalization performance. Multi-view learning is also known as data fusion or data integration from multiple feature sets. Since the last survey of multi-view machine learning in early 2013, multi-view learning has made great progress and developments in recent years, and is facing new challenges. This overview first reviews theoretical underpinnings to understand the properties and behaviors of multi-view learning. Then multi-view learning methods are described in terms of three classes to offer a neat categorization and organization. For each category, representative algorithms and newly proposed algorithms are presented. The main feature of this survey is that we provide comprehensive introduction for the recent developments of multi-view learning methods on the basis of coherence with early methods. We also attempt to identify promising venues and point out some specific challenges which can hopefully promote further research in this rapidly developing field.