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

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.

Authors:
; ;
Award ID(s):
1651902
Publication Date:
NSF-PAR ID:
10065420
Journal Name:
IJCAI
Page Range or eLocation-ID:
5434 to 5440
Sponsoring Org:
National Science Foundation
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