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Analysis of high dimensional data is a common task. Often, small multiplesare used to visualize 1 or 2 dimensions at a time, such as in a scatterplotmatrix. Associating data points between different views can be difficultthough, as the points are not fixed. Other times, dimensional reductiontechniques are employed to summarize the whole dataset in one image, butindividual dimensions are lost in this view. In this paper, we present a meansof augmenting a dimensional reduction plot with isocontours to reintroduce theoriginal dimensions. By applying this to each dimension in the original data,we create multiple views where the points are consistent, which facilitatestheir comparison. Our approach employs a combination of a novel, graph-basedprojection technique with a GPU accelerated implementation of moving leastsquares to interpolate space between the points. We also present evaluations ofthis approach both with a case study and with a user study.

A Moving Least Squares Based Approach for Contour Visualization of Multi-Dimensional Data