关键词: 半监督分类, Universum方法, 线性回归, 子空间学习

Abstract: Classification is an important branch of machine learning. It remains a hot issue how to attain a better classification with less labeled data in recent research. Traditional semi-supervised classification can take advantage of the training samples, either labeled or unlabeled, but ignores related non-samples, called the Universum. Combining the advantage of traditional semi-supervised methods and the Universum, Semi-Supervised Classification with the Universum（SSCU） via linear regression and subspace learning, can effectively improve the classification of original high-dimensional data adding no labels. The effectiveness is verified by both simulation and real-world data.

Key words: semi-supervised classification, Universum, linear regression, subspace learning