Robust Multi-view Representation: A Unified Perspective from Multi-view Learning to Domain Adaption

Robust Multi-view Representation: A Unified Perspective from Multi-view Learning to Domain Adaption

Zhengming Ding, Ming Shao, Yun Fu

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Survey track. Pages 5434-5440. https://doi.org/10.24963/ijcai.2018/767

Multi-view data are extensively accessible nowadays thanks to various types of features, different view-points and sensors which tend to facilitate better representation in many key applications. This survey covers the topic of robust multi-view data representation, centered around several major visual applications. First of all, we formulate a unified learning framework which is able to model most existing multi-view learning and domain adaptation in this line. Following this, we conduct a comprehensive discussion across these two problems by reviewing the algorithms along these two topics, including multi-view clustering, multi-view classification, zero-shot learning, and domain adaption. We further present more practical challenges in multi-view data analysis. Finally, we discuss future research including incomplete, unbalance, large-scale multi-view learning. This would benefit AI community from literature review to future direction.
Keywords:
Machine Learning: Classification
Machine Learning: Transfer, Adaptation, Multi-task Learning
Machine Learning: Multi-instance;Multi-label;Multi-view learning
Machine Learning: Clustering