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 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…

• RG
  Research Grants Council, University Grants Committee

  Award: CUHK 2300174
• NK
  National Key Research and Development Program of China

  Award: 2018AAA0100204
• SS
  Shenzhen Science and Technology Innovation Program

  Award: JCYJ20200109113403826