计算机工程与应用2011，Vol.47Issue(14)：124-127,145,5.DOI:10.3778/j.issn.1002-8331.2011.14.035
基于最优邻域图的等距映射流形学习算法
Improved isometric mapping algorithm for manifold learning based on optimal neighborhood graph.
摘要
Abstract
The recent isometric mapping algorithms are sensitive to selecting an appropriate neighborhood size,and present insufficient noise tolerance. Based on the relationship of the average shortest distance with the neighborhood size and the average shortest distance gradient,this paper proposes a new method for constructing the optimal neighbor graph from a data set,which has few short-circuit edges,and better approximates the geodesic distances between the data points.Furthermore,for different points the neighborhood sizes are adaptive variant with the local characteristics of the data points. Experimental results show that the proposed method yields better performances for symmeetrically sampling data points free of noise than the recent methods. It is also shown that the topologically stability and degree of noise tolerance can be significantly improved.关键词
邻域图/平均最短路径/平均最短路径梯度/测地距/等距映射Key words
the neighborhood graphs/average shortest distance/average shortest distant gradient/geodesic distance/isometric mapping分类
信息技术与安全科学引用本文复制引用
张银凤,王晅 ,马建峰..基于最优邻域图的等距映射流形学习算法[J].计算机工程与应用,2011,47(14):124-127,145,5.基金项目
陕西省自然科学基础研究计划基金(No.2009JM8002). （No.2009JM8002）
