Graph-Based Semi-Supervised Learning: A Comprehensive Review

Semi-supervised learning (SSL) has tremendous value in practice due to the utilization of both labeled and unlabelled data. An essential class of SSL methods, referred to as graph-based semi-supervised learning (GSSL) methods in the literature, is to first represent each sample as a node in an affin...

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Vydáno v:	IEEE transaction on neural networks and learning systems Ročník 34; číslo 11; s. 8174 - 8194
Hlavní autoři:	Song, Zixing, Yang, Xiangli, Xu, Zenglin, King, Irwin
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States IEEE 01.11.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Codes Graph embedding graph representation learning graph-based semi-supervised learning (GSSL) Image color analysis Learning Manifolds Prediction algorithms Semi-supervised learning semi-supervised learning (SSL) Semisupervised learning Taxonomy Training
ISSN:	2162-237X, 2162-2388, 2162-2388
On-line přístup:	Získat plný text
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Popis
Shrnutí:	Semi-supervised learning (SSL) has tremendous value in practice due to the utilization of both labeled and unlabelled data. An essential class of SSL methods, referred to as graph-based semi-supervised learning (GSSL) methods in the literature, is to first represent each sample as a node in an affinity graph, and then, the label information of unlabeled samples can be inferred based on the structure of the constructed graph. GSSL methods have demonstrated their advantages in various domains due to their uniqueness of structure, the universality of applications, and their scalability to large-scale data. Focusing on GSSL methods only, this work aims to provide both researchers and practitioners with a solid and systematic understanding of relevant advances as well as the underlying connections among them. The concentration on one class of SSL makes this article distinct from recent surveys that cover a more general and broader picture of SSL methods yet often neglect the fundamental understanding of GSSL methods. In particular, a significant contribution of this article lies in a newly generalized taxonomy for GSSL under the unified framework, with the most up-to-date references and valuable resources such as codes, datasets, and applications. Furthermore, we present several potential research directions as future work with our insights into this rapidly growing field.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	2162-237X 2162-2388 2162-2388
DOI:	10.1109/TNNLS.2022.3155478