关键词: 半监督降维, 流形假设, 约束对, 目标函数, 聚类分析

Abstract: Most existing typical semi-supervised dimensionality reduction algorithms often ignore the manifold features of data or use them inappropriately，while focusing on how to use supervised information.Therefore those algorithms show poor performance and can not be used in many fields.This paper presents a semi-supervised complex structure data dimensionality reduction method，called CSDDR，which keeps the global and local structure of the whole data manifold in the low dimensional embedding subspace.An appropriate objective function makes the method has more areas of application，and the experimental results show the effectiveness of the method.

Key words: Semi-Supervised Dimensionality Reduction（SSDR）, manifold assumption, constraints, objective function, clustering analysis