Kernel based Incremental Learning Isomap Algorithm

摘要：

Isomap is one of widely-used low-dimensional embedding methods.However,in many scenarios,the data come sequentially and the effect of the data is accumulated.Isomap algorithms have no the ability of new data be added for all data need to be available when estimates the geodesic distances.In this paper we propose an incremental learning Isomap algorithm, which take the approximate geodesic distance matrix as a kernel matrix then projection is converted to solve a kernel eigenvalues problem.We have no need to reconstructing the neighborhood graph for those incremental samples.Experiments results on Swiss datasets and Helix datasets show that our algorithm is more effective than Isomap method.

作者: Ying Zhang Yaonan Wang Chunsheng Li Kena Wang

作者单位: Department of Electrical and Information Engineering Hunan University Changsha,Hunan Province,China

会议类型: 国际会议

会议名称: 2008 IEEE International Conference on Onformation and Automation(IEEE 信息与自动化国际会议)

会议地点: 张家界

会议语种:英文

页码: 184-189

在线出版日期: 2008-06-20（万方平台首次上网日期，不代表论文的发表时间）