Interactive Dimensionality Reduction Through User-defined Combinations of Quality Metrics

Abstract

Multivariate data sets including hundreds of variables are increasingly common in many application areas. Most multivariate visualization techniques are unable to display such data effectively, and a common approach is to employ dimensionality reduction prior to visualization. Most existing dimensionality reduction systems focus on preserving one or a few significant structures in data. For many analysis tasks, however, several types of structures can be of high significance and the importance of a certain structure compared to the importance of another is often task-dependent. This paper introduces a system for dimensionality reduction by combining user-defined quality metrics using weight functions to preserve as many important structures as possible. The system aims at effective visualization and exploration of structures within large multivariate data sets and provides enhancement of diverse structures by supplying a range of automatic variable orderings. Furthermore it enables a quality-guided reduction of variables through an interactive display facilitating investigation of trade-offs between loss of structure and the number of variables to keep. The generality and interactivity of the system is demonstrated through a case scenario.

Representative Factor Generation for the Interactive Visual Analysis of High-Dimensional Data

“…Endert et al [6] introduce observation level interactions to assist computational analysis tools to deliver more reliable results. Johansson and Johansson [20] enable the user to interactively reduce the dimensionality of a dataset with the help of quality metrics. In these works, interactive methods are usually used to refine certain parameters for the use of computational tools.…”

Result-Driven Exploration of Simulation Parameter Spaces for Visual Effects Design

“…Dimensionality reduction and clustering are commonly employed for gaining insight into high-dimensional parameter spaces [6,21]. Our input parameter space is also multi-dimensional, but we apply clustering to characterize the output space of simulations to extract information about visual variations over time.…”

Towards Perceptual Optimization of the Visual Design of Scatterplots

“…Yet these techniques optimize only one design parameter (i.e., class color). Johansson and Johansson [26] demonstrated an interactive technique to reduce dimensionality via quality metrics. They used weight functions to preserve as many important structures as possible for exploration, along with quality metrics for class separation, outliers, and correlation in the data space -but not in the image space as in our approach.…”