Experimental Comparisons of Semi-Supervised Dimensional Reduction Methods

Abstract

半监督学习是近年来机器学习领域中的研究热点之一,已从最初的半监督分类和半监督聚类拓展到半监督回归和半监督降维等领域.目前,有关半监督分类、聚类和回归等方面的工作已经有了很好的综述,如Zhu 的半监督学习文献综述.降维一直是机器学习和模式识别等相关领域的重要研究课题,近年来出现了很多将半监督思想用于降维,即半监督降维方面的工作.有鉴于此,试图对目前已有的一些半监督降维方法进行综述,然后在大量的标准数据集上对这些方法的性能进行实验比较,并据此得出了一些经验性的启示.;Semi-Supervised learning is one of the hottest research topics in the technological community, which has been developed from the original semi-supervised classification and semi-supervised clustering to the semi-supervised regression and semi-supervised dimensionality reduction, etc. At present, there have been several excellent surveys on semi-supervised classification: Semi-Supervised clustering and semi-supervised regression, e.g. Zhu’s semi-supervised learning literature survey. Dimensionality reduction is one of the key issues in machine learning, pattern recognition, and other related fields. Recently, a lot of research has been done to integrate the idea of semi-supervised learning into dimensionality reduction, i.e. semi-supervised dimensionality reduction. In this paper, the current semi-supervised dimensionality reduction methods are reviewed, and their performances are evaluated through extensive experiments on a large number of benchmark datasets, from which some empirical insights can be obtained.