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Multi-view learning overview: Recent progress and new challenges
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“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]
In the citing paper
“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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Self-citation
Metadata
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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