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

TL;DR

This paper introduces a novel visualization method combining graph-based projection and GPU-accelerated moving least squares to enhance contour visualization of high-dimensional data, improving interpretability and comparison across multiple views.

Contribution

It presents a new approach that augments dimensional reduction plots with isocontours using a graph-based projection and GPU acceleration, enabling consistent multi-view visualization of high-dimensional data.

Findings

Effective visualization of high-dimensional data with isocontours.

Improved data point consistency across multiple views.

Positive user study feedback on interpretability.

Abstract

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.