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Lespinats, S., Verleysen, M., Giron, A. and Fertil, B. (2007) “DD-HDS: a tool for visualization and exploration of highdimensional data.” IEEE transactions on Neural Networks, 18(5), pp 1265-1279.

Abstract :
Visualization of high-dimensional data is generally achieved by a projection into a low (usually 2- or 3-) dimensional space. Visualization is intended to facilitate the understanding of datasets by preserving some "essential" information. This paper presents DD-HDS (Data Driven High-Dimensional Scaling), a non-linear Multi-Dimensional Scaling (MDS) method relying on the Force Directed Placement (FDP) paradigm to help dynamically discover features of interest in data sets. Through a specific weighting of distances taking into account the concentration of measures phenomenon, and a symmetric handling of short distances in the original and output spaces, the method is particularly adapted to the projection of high-dimensional data. A single user-defined parameter in the optimization procedure implements the compromise between local neighborhood preservation and global mapping. The projection of low- and high-dimensional examples illustrates the features and advantages of the proposed algorithm.

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