Robust Laplacian eigenmap
鲁棒拉普拉斯特征映射算法*

This paper focused on the sensitivity of Laplacian eigenmap (LE) to outliers, and presented a robust Laplacian eigenmap (RLE). RLE was base on the outlier detection, projected the outliers and their neighbors to the low-dimensional tangent space with the robust PCA method. In the low-dimensional tangent space, RLE constructed the to weight graph connected the outliers and their neighbors, which could reflect the intrinsic local geometry of the outliers.The algorithm reduced the impact of outliers on the Laplacian matrix. Simulation and real examples show that RLE is robust against outliers.