哈尔滨工程大学学报2012，Vol.33Issue(4)：489-495,7.DOI:10.3969/j.issn.1006-7043.201105053
自适应近邻的局部线性嵌入算法
Adaptive neighborhoods based locally linear embedding algorithm
摘要
Abstract
Finding the optimal neighbors in a locally linear embedding (LLE) algorithm is still an unresolved problem. Trial and error, a commonly used method, requires much time to obtain the optimal result. In this paper, an adaptive neighborhoods based locally linear embedding algorithm ( ANLLE) was proposed which first provided a new similarity measure function and secondly set a threshold for each sample. Then, it set various numbers of neighbors for each sample according to different distributions around it. Finally, the ANLLE reduced the dimension of samples and classified them to be tested in the case of different neighbors for each sample. A comparison of ANLLE , the neighborhood linear embedding algorithm ( NLE) , and a standard LLE algorithm in human faces and script databases proves that the ANLLE is more effective than standard LLE and NLE algorithms.关键词
局部线性嵌入/自适应近邻/维数约减/嵌入算法/最优近邻/相似性度量函数Key words
locally linear embedding/ adaptive neighborhoods/ dimensionality reduction/ embedding algorithm/ optimal neighbors/ similarity measure function分类
信息技术与安全科学引用本文复制引用
张兴福,黄少滨..自适应近邻的局部线性嵌入算法[J].哈尔滨工程大学学报,2012,33(4):489-495,7.基金项目
国家自然科学基金资助项目(60873038). （60873038）
