南京理工大学学报（自然科学版）2017，Vol.41Issue(6)：748-752,5.DOI:10.14177/j.cnki.32-1397n.2017.41.06.013
基于自适应邻域选择的局部线性嵌入算法
Locally linear embedding algorithm based on adaptive neighborhood selection
摘要
Abstract
An improved locally linear embedding ( LLE ) algorithm based on local neighborhood-dependent weights and sparse matrices is proposed to improve the computation efficiency of dimensionality reduction for high-dimensional data. The correlation dimension estimation method is used to estimate the intrinsic information of the dimensionality reduction in high-dimensional data and the upper bound of the uniform manifold. Five classical datasets,including Swiss,Broken swiss, Helix,Twinpeaks and Intersect, are used to assess the algorithm. The results show that, compared with that of local linear embedding algorithm, the calculation speed of this algorithm on the five datasets is improved by 27. 60％,27. 51％,27. 18％,28. 31％ and 45. 28％ respectively.关键词
自适应邻域选择/局部线性嵌入/稀疏矩阵/数据降维/流形算法Key words
adaptive neighborhood selection/locally linear embedding/sparse matrices/dimension reduction/manifold algorithm分类
信息技术与安全科学引用本文复制引用
张志友,周佳燕,邵海见,鲍安平..基于自适应邻域选择的局部线性嵌入算法[J].南京理工大学学报（自然科学版）,2017,41(6):748-752,5.基金项目
江苏省高等学校自然科学研究项目(17KJB470002) （17KJB470002）
