Abstract
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
