Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (6): 19-27.DOI: 10.3778/j.issn.1002-8331.1911-0083

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Review of Semi-Supervised Learning Research

HAN Song, HAN Qiuhong   

  1. School of Information, Beijing Wuzi University, Beijing 101149, China
  • Online:2020-03-15 Published:2020-03-13



  1. 北京物资学院 信息学院,北京 101149


Traditional supervised learning methods require a lot of labeled samples to accomplish training tasks, but in practical application, the collection of labeled samples is difficult. Although unsupervised learning methods do not require prior information, it is difficult to guarantee the accuracy. Semi-supervised learning breaks through the limitation of traditional methods that only consider labeled samples or unlabeled samples, and can mine a large amount of information hidden in unlabeled data and assist a small number of labeled samples for training, becoming a research hotspot of machine learning. This paper summarizes the general trend and detailed research contents of domestic semi-supervised learning, and summarizes six aspects including semi-supervised clustering, classification, regression and dimension reduction, unbalanced data classification and noise reduction. There are many semi-supervision methods, but there are some shortcomings:(1)Although some of the new and effective methods are proposed, they are only verified through specific data sets and lack theoretical basis and proof. (2)When the data is complex, the semi-supervised model needs many parameters, but lacks the experience of parameter selection. (3)The supervision information is mostly in the form of sample labels or pair constraints, and the semi-supervised learning of mixed constraints needs further study. (4)There is a lack of research on semi-supervised regression and the prior information of continuous variables.

Key words: semi-supervised learning, semi-supervised clustering, semi-supervised classification, semi-supervised dimension reduction, semi-supervised regression



关键词: 半监督学习, 半监督聚类, 半监督分类, 半监督降维, 半监督回归