Robust ISOMAP insensitive to singular value
一种对奇异值不敏感的ISOMAP

ISOAMP is a classical nonlinear dimensionality reduction algorithm. It is effective to discover the low-dimensional manifold in a high-dimensional data space. But the algorithm is very sensitive to the noises and singular value. Principal Component Analysis with robustness (Robust PCA) was used to detect singular points, and the singularity was also appropriately treated to reduce the ISOMAP's sensitivity to it. The proposed algorithm is intuitive and easy to understand, the results of the experiment prove that it is robust, and can maintain the overall structure of data with more singular points.