The Community for Technology Leaders
Green Image
A distance-preserving method is presented to map high-dimensional data sequentially to low-dimensional space. It preserves exact distances of each data point to its nearest neighbor and to some other near neighbors. Intrinsic dimensionality of data is estimated by examining the preservation of interpoint distances. The method has no user-selectable parameter. It can successfully project data when the data points are spread among multiple clusters. Results of experiments show its usefulness in projecting high-dimensional data.
Pattern recognition, statistical, feature evaluation and selection, pattern analysis.

L. Yang, "Distance-Preserving Projection of High-Dimensional Data for Nonlinear Dimensionality Reduction," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 26, no. , pp. 1243-1246, 2004.
79 ms
(Ver 3.3 (11022016))