Experimental comparisons of semi-supervised dimensional reduction methods

Abstract

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. © Copyright 2011, Institute of Software, the Chinese Academy of Sciences. All rights reserved.