INCREMENTAL SELECTION OF THE NEIGHBORHOOD SIZE FOR ISOMAP
The success of ISOMAP depends greatly on selecting a suitable neighborhood size; however, its an open problem how to do this efficiently. When the neighborhood size becomes unsuitable, shortcut edges can be introduced into the neighborhood graph and destroy the approximation ability of the involved shortest-path distances to the corresponding geodesic distances greatly. Its obvious that shortcut edge links two endpoints lying close in Euclidean space but far away on the manifold, which can be measured approximately by its order presented in this paper. Based on the observation, this paper presented an efficient method to find a suitable neighborhood size incrementally, which doesnt need to compute shortest-path distances or run the MDS algorithm as those methods based on residual variance do. Finally, the feasibility of this method can be verified by experimental results.
Data visualization ISOMAP Neighborhood size Residual variance Order
CHAO SHAO
School of Information, Henan University of Finance and Economics, Zhengzhou 450002, China
国际会议
2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)
昆明
英文
436-441
2008-07-12（万方平台首次上网日期，不代表论文的发表时间）