Multi-view learning overview: Recent progress and new challenges
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Multiview Learning constructs different views from multiple sources or feature subsets and jointly optimizes models to enhance generalization, especially with weak supervision.
“Finally, Multiview Learning [417] constructs different views of the object as per the information contained in the different data sources”
From Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI (2020) · cites this paper as [419]
Several machine learning specialisations, including reinforcement, hybrid, multi-view and self-supervised learning, are appearing in general healthcare but have not yet gained sufficient traction in multi-omics analysis.
“Although various ML specialisations such as reinforcement (Coronato et al., 2020), hybrid (Zhou et al., 2019), multi-view (Zhao et al., 2017) and selfsupervised learning (Chen et al., 2019) are now emerging in generic health-care applications, they have not yet gained enough momentum in multi-omics analysis, hence they remain beyond the scope of this review.”
From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Zhao et al., 2017
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Latent information-guided one-step multi-view fuzzy clustering based on cross-view anchor graph
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Adaptive collaborative fusion for multi-view semi-supervised classification
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A Multi-View Deep Learning Approach for Predictive Business Process Monitoring
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Data-Driven Modeling of Pregnancy-Related Complications
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Self-citation
- Authors
- Zhao, J., Xie, X., Xu, X., & Sun, S.
- Affiliations
- East China Normal University (CN)
- National University of Defense Technology (CN)
- Corresponding author
- Sun, Shiliang
- Venue
- Information Fusion
- Publisher
- Elsevier BV
- Published
- 2017-11
- Volume
- 38
- Pages
- 43-54
- ISSN
- 1566-2535 (ISSN-L)
- OpenAlex
- W2590019597
- Cited by
- 1,002
- References
- 172
- Citations by year
- 2026: 90 · 2025: 104 · 2024: 132 · 2023: 119 · 2022: 125 · 2021: 120 · 2020: 128 · 2019: 98 · 2018: 70 · 2017: 16
- FWCI
- 50.89
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Information Fusion (publishedVersion)
- Funders
- National Natural Science Foundation of China (61673179, 61370175)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Keywords
- multi-view learning, data fusion, data integration, generalization performance
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