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A survey on semi-supervised learning
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Abstract Semi-supervised learning is the branch of machine learning concerned with using labelled as well as unlabelled data to perform certain learning tasks. Conceptually situated between supervised and unsupervised learning, it permits harnessing the large amounts of unlabelled data available in many use cases in combination with typically smaller sets of labelled data. In recent years, research in this area has followed the general trends observed in machine learning, with much attention directed at neural network-based models and generative learning. The literature on the topic has also expanded in volume and scope, now encompassing a broad spectrum of theory, algorithms and applications. However, no recent surveys exist to collect and organize this knowledge, impeding the ability of researchers and engineers alike to utilize it. Filling this void, we present an up-to-date overview of semi-supervised learning methods, covering earlier work as well as more recent advances. We focus primarily on semi-supervised classification, where the large majority of semi-supervised learning research takes place. Our survey aims to provide researchers and practitioners new to the field as well as more advanced readers with a solid understanding of the main approaches and algorithms developed over the past two decades, with an emphasis on the most prominent and currently relevant work. Furthermore, we propose a new taxonomy of semi-supervised classification algorithms, which sheds light on the different conceptual and methodological approaches for incorporating unlabelled data into the training process. Lastly, we show how the fundamental assumptions underlying most semi-supervised learning algorithms are closely connected to each other, and how they relate to the well-known semi-supervised clustering assumption.
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“Semi-supervised learning uses both labelled and unlabeled data to perform specific tasks [9].”
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“Semisupervised techniques include expectation maximization with generative mixture models, transductive SVMs, graph-based methods, and so on [39], [40].”
BackgroundMethod
Metadata
- Authors
- van Engelen, J. E., & Hoos, H. H.
- Affiliations
- Leiden University (NL)
- Venue
- Machine Learning
- Published
- 2019-11-15
- Volume
- 109
- Issue
- 2
- Pages
- 373-440
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W2984353870
- Cited by
- 2,666
- References
- 211
- Citations by year
- 2026: 292 · 2025: 501 · 2024: 467 · 2023: 558 · 2022: 464 · 2021: 313 · 2020: 68 · 2019: 3
- FWCI
- 96.74
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Machine Learning (publishedVersion, cc-by)
- Funders
- Leiden University
- 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 › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- semi-supervised learning, semi-supervised classification, unlabeled data, generative models
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