{"type":"ScholarlyArticle","title":"Robust Multi-view Representation: A Unified Perspective from Multi-view Learning to Domain Adaption","authors":[{"name":"Zhengming Ding","slug":"zhengming-ding","given":"Zhengming","family":"Ding","affiliation":"Computer and Information Science , University of Massachusetts Dartmouth , USA"},{"name":"Ming Shao","slug":"ming-shao","given":"Ming","family":"Shao","affiliation":"‡ Department of ECE , College of Engineering , Northeastern University , Boston , USA"},{"name":"Yun Fu","slug":"yun-fu","given":"Yun","family":"Fu","affiliation":"College of Computer and Information Science , Northeastern University , Boston , USA"}],"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.","doi":"10.24963/ijcai.2018/767","url":"https://ijcai.org/proceedings/2018/767","pdf":"https://ijcai.org/proceedings/2018/0767.pdf","markdown":"https://ijcai.org/proceedings/2018/767.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/767","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Survey track","track":"Survey track","number":767,"pages":{"first":5434,"last":5440},"keywords":["Machine Learning: Classification","Machine Learning: Transfer, Adaptation, Multi-task Learning","Machine Learning: Clustering","Machine Learning: Multi-instance;Multi-label;Multi-view learning"],"videos":[],"references":[{"position":1,"raw":"[Andrew et al., 2013] Galen Andrew, Raman Arora, Jeff Bilmes, and Karen Livescu. 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