计算机工程与应用2011，Vol.47Issue(2)：156-159,4.DOI:10.3778/j.issn.1002-8331.2011.02.048
改进的非线性数据降维方法及其应用
Improved non-linear data dimensionality reduction method and its application
摘要
Abstract
Locally Linear Embedding(LLE) algorithm is one of the non-linear dimensionality reduction methods which are based on manifold learning. In LLE, each sample point is reconstructed from a linear combination of its nearest neighbors.However, different number of neighbors will produce different reconstruction errors, which will make the result different directly. This paper structures the approximate reconstruction coefficient making use of their category information which is obtained by clustering, and proposes an improved algorithm.The proposed algorithm can reduce the influence of the number of neighbors efficiently and the probability of the database is retained. This is confirmed by experiments on both synthetic and real-world data.关键词
数据降维/流形学习/局部线性嵌入/图像检索Key words
data dimensionality reduction/ manifold learning/ locally linear embedding /image retrieval分类
信息技术与安全科学引用本文复制引用
吴晓婷,闫德勤..改进的非线性数据降维方法及其应用[J].计算机工程与应用,2011,47(2):156-159,4.基金项目
国家自然科学基金(the National Natural Science Foundation of China under Grant No.60372071) （the National Natural Science Foundation of China under Grant No.60372071）
中国科学院自动化研究所复杂系统与智能科学重点实验室开放课题基金(No.20070101) （No.20070101）
辽宁省教育厅高等学校科学研究基金(No.2004C031) （No.2004C031）
大连市科技局科技计划项目(NO.2007A10GX117). （NO.2007A10GX117）
