Distance-preserving projection of high dimensional data

Authors:
Li Yang
Affiliations:
Department of Computer Science, Western Michigan University, 1903 W Michigan Avenue, Kalamazoo, MI
Venue:
Pattern Recognition Letters
Year:
2004

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Abstract

This paper presents a distance-preserving method of mapping high dimensional data to low spaces. The low-dimensional configuration preserves exact distances of each data point to some of its near neighbors. Unlike other nonlinear mapping methods which need at least a parameter to specify the size of neighborhoods to look around, this method has no user-selectable parameter and thus has no shortcut problem. It also works well when data are spread among multiple clusters. The method is demonstrated through examples on both synthetic and real world data.