Experimental Comparisons of Semi Supervised Dimensional Reduction Methods
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.