文档介绍：软件学报 ISSN 1000-9825, CODEN RUXUEW E-mail: ******@iscas.
Journal of Software,2011,22(1):28−43 [doi: .]
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半监督降维方法的实验比较
陈诗国, 张道强+
(南京航空航天大学计算机科学与工程系,江苏南京 210016)
parisons of Semi-Supervised Dimensional Reduction Methods
CHEN Shi-Guo, ZHANG Dao-Qiang+
(Department puter Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)
+ Corresponding author: E-mail: ******@nuaa.
Chen SG, Zhang DQ. parisons of semi-supervised dimensional reduction methods. Journal
of Software, 2011,22(1):28−43. /1000-9825/
Abstract: Semi-Supervised learning is one of the hottest research topics in the munity, 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, .
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, . 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.
Key words: semi-supervised dimensionality reduction; dimensionality reduction; semi-supervised learning; class
label; pairwise constraint
摘要: 半监督学****是近年来机器学****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
,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
,近年来出现了很