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High Performance Dimension Reduction and Visualization for Large High-Dimensional Data Analysis

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Executive Summary

Large high dimension datasets are of growing importance in many fields and it is important to be able to visualize them for understanding the results of data mining approaches or just for browsing them in a way that distance between points in visualization (2D or 3D) space tracks that in original high dimensional space. Dimension reduction is a well understood approach but can be very time and memory intensive for large problems. Here the author reports on parallel algorithms for Scaling by MAjorizing a COmplicated Function (SMACOF) to solve Multidimensional Scaling problem and Generative Topographic Mapping (GTM).

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