  Analysis of high dimensional data is a common task. Often, small multiples
are used to visualize 1 or 2 dimensions at a time, such as in a scatterplot
matrix. Associating data points between different views can be difficult
though, as the points are not fixed. Other times, dimensional reduction
techniques are employed to summarize the whole dataset in one image, but
individual dimensions are lost in this view. In this paper, we present a means
of augmenting a dimensional reduction plot with isocontours to reintroduce the
original dimensions. By applying this to each dimension in the original data,
we create multiple views where the points are consistent, which facilitates
their comparison. Our approach employs a combination of a novel, graph-based
projection technique with a GPU accelerated implementation of moving least
squares to interpolate space between the points. We also present evaluations of
this approach both with a case study and with a user study.
