南京大学学报（自然科学版）2009，Vol.45Issue(5)：593-603,11.
子模式判别型半监督非线性维数减少算法
Sub-pattern-based discriminative semi-supervised nonlinear dimensionality reduction
摘要
Abstract
Dimensionality reduction is an important preprocessing step in high-dimensional image data analysis and has become a hot issue in dealing with high-dimensional image data without losing the intrinsic information. The problem of the discriminative semi-supervised nonlinear dimensionality reduction (DSSNDR) is discussed to exploit the labeled and unlabeled data samples at the same time. DSSNDR can project the input samples to a set of more useful features in the low dimensional feature subspace and can preserve the intrinsic structure of the original data, under which the data are easier to be effectively partitioned from each other. In this setting, within-class and between-class scatter information are adopted to specify whether samples belong to the same class or different classes. To improve the performances of DSSNDR further, we also propose Sp-DSSNDR for dimensionality reduction, which performs DSSNDR on the sub-patterns of the original data. We also demonstrate the practical usefulness and high scalability of the DSSNDR and Sp-DSSNDR methods in data visualization and classification tasks through extensive simulation studies. Experimental results show that DSSNDR and Sp-DSSNDR can almost always achieve the highest accuracy when the dimension of the data is reduced to a lower level. In most cases, performances of the proposed algorithms outperform those of many established typical dimensionality reduction methods, such as principle comporent analysis (PCA) , kernel principle component analysis ( KPCA) and kernel fisher discriminant analysis(KFD).关键词
半监督学习/子模式/维数减少/特征子空间/判别分析Key words
semi-supervised learning/ sub-pattern/ dimensionality reduction/ feature subspace/ discriminant analysis分类
信息技术与安全科学引用本文复制引用
张召,业宁,杜辉,沈丽容,张贤涛..子模式判别型半监督非线性维数减少算法[J].南京大学学报（自然科学版）,2009,45(5):593-603,11.基金项目
国家自然科学基金(30671639).江苏省自然科学基金(BK2009393),江苏省高校科技创新计划(164070265),江苏省研究生创新基金,南京林业大学科技创新项目 （30671639）
