关键词: 非线性降维, 共享近邻, 等距嵌入算法, 离群点, 绩效考核

Abstract: Manifold learning algorithm ISOMAP is sensitive to the outliers. To solve this problem, the paper employed the distance measurement based on shared nearest neighbor and made a full use of the local density information of points on the manifold, which resulted in an effective improvement on the robustness of the algorithm. Meanwhile, the paper first attempted to apply the improved manifold learning algorithm to the hospital performance evaluation. The experiments on the artificial data and real-world data show that the improved algorithm is robust and effective, and the application to the performance evaluation is successful.

Key words: nonlinear dimensionality reduction, shared nearest neighbor, Isometric Mapping (ISOMAP), outlier, performance evaluation