数据采集与处理2017，Vol.32Issue(3)：559-569,11.DOI:10.16337/j.1004-9037.2017.03.015
基于随机游走的流形学习与可视化
Manifold Learning and Visualization Based on Random Walk
摘要
Abstract
The existing global manifold learning algorithms are relatively sensitive to the neighborhood size,which is difficult to select efficiently.The reason is mainly because the neighborhood graph is constructed based on Euclidean distance,by which shortcut edges tend to be introduced into the neighborhood graph.To overcome this problem,a global manifold learning algorithm is proposed based on random walk,called the random walk-based isometric mapping (RW-ISOMAP).Compared with Euclidean distance,the commute time distance,achieved by the random walk on the neighborhood graph,can measure the similarity between the given data within the nonlinear geometric structure to a certain extent,thus it can provide robust results and is more suitable to construct the neighborhood graph.Consequently,by constructing the neighborhood graph based on the commute time distance,RW-ISOMAP is less sensitive to the neighborhood size and more robust than the existing global manifold learning algorithms.Finally,the experiment verifies the effectiveness of RW-ISOMAP.关键词
全局流形学习/等距映射/邻域图/随机游走/通勤时间距离Key words
global manifold learning/isometric mapping/neighborhood graph/random walk/commute time distance分类
信息技术与安全科学引用本文复制引用
邵超,万春红,张啸剑..基于随机游走的流形学习与可视化[J].数据采集与处理,2017,32(3):559-569,11.基金项目
国家自然科学基金(61202285)资助项目 （61202285）
河南省教育厅科学技术研究重点(14B520020)资助项目. （14B520020）
