Quantitative Evaluation of Time-Dependent Multidimensional Projection Techniques

TL;DR

This paper evaluates various dimensionality reduction techniques for dynamic multivariate data, analyzing their visual quality and stability to identify the most effective methods for time-dependent datasets.

Contribution

It introduces an experimental framework with datasets and metrics to assess projection techniques specifically for time-dependent data, including new variations of static methods.

Findings

Identified the most suitable projection methods for dynamic data

Provided a comprehensive dataset collection for evaluation

Analyzed stability and quality trade-offs of methods

Abstract

Dimensionality reduction methods are an essential tool for multidimensional data analysis, and many interesting processes can be studied as time-dependent multivariate datasets. There are, however, few studies and proposals that leverage on the concise power of expression of projections in the context of dynamic/temporal data. In this paper, we aim at providing an approach to assess projection techniques for dynamic data and understand the relationship between visual quality and stability. Our approach relies on an experimental setup that consists of existing techniques designed for time-dependent data and new variations of static methods. To support the evaluation of these techniques, we provide a collection of datasets that has a wide variety of traits that encode dynamic patterns, as well as a set of spatial and temporal stability metrics that assess the quality of the layouts. We present an evaluation of 11 methods, 10 datasets, and 12 quality metrics, and elect the best-suited methods for projecting time-dependent multivariate data, exploring the design choices and characteristics of each method. All our results are documented and made available in a public repository to allow reproducibility of results.