A VERSION OF ISOMAP WITH EXPLICIT MAPPING

摘要：

Recently several manifold learning algorithms have been presented for nonlinear dimensionality reduction. Isomap is one of them. However, Isomap suffers from a deficiency that it does not give an explicit mapping function, which is from high dimensional space to low dimensional target space. In this paper, a version of Isomap with explicit mapping, called E-Isomap, is proposed. In E-Isomap, the geodesic distance matrix is fed into a cost function and then Iterative Majorization is adopted to solve an optimization problem for obtaining both the low dimensional configuration and the nonlinear mapping. Owing to the existence of explicit mapping, this version of Isomap can be more easily used in pattern recognition than the original ones. The experiments on two benchmark data sets are given to demonstrate the performance of the presented method.

关键词： Nonlinear Dimensionality Reduction Manifold Learning Isomap Geodesic Distance E-Isomap

作者: CHUN-GUANG LI JUN GUO GUANG CHEN XIANG-FEI NIE ZHEN YANG

作者单位: Pattern Recognition and Intelligent System Lab, School of Information Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China

会议类型: 国际会议

会议名称: 2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

会议地点: 大连

会议语种:英文

页码: 3201-3206

在线出版日期: 2006-08-13（万方平台首次上网日期，不代表论文的发表时间）