Progressive Glimmer: Expanding Dimensionality in Multidimensional Scaling

TL;DR

Progressive Glimmer introduces a novel multidimensional scaling algorithm that incrementally increases data dimensionality, enabling stable, visually consistent embeddings suitable for streaming and spatio-temporal data analysis.

Contribution

It adapts the Glimmer algorithm for progressive updates in data dimensionality, enhancing visualization stability and applicability to streaming and spatio-temporal datasets.

Findings

Provides more stable and consistent embeddings

Enables progressive visualization for streaming data

Applicable to spatio-temporal simulation ensembles

Abstract

Progressive dimensionality reduction algorithms allow for visually investigating intermediate results, especially for large data sets. While different algorithms exist that progressively increase the number of data points, we propose an algorithm that allows for increasing the number of dimensions. Especially in spatio-temporal data, where each spatial location can be seen as one data point and each time step as one dimension, the data is often stored in a format that supports quick access to the individual dimensions of all points. Therefore, we propose Progressive Glimmer, a progressive multidimensional scaling (MDS) algorithm. We adapt the Glimmer algorithm to support progressive updates for changes in the data's dimensionality. We evaluate Progressive Glimmer's embedding quality and runtime. We observe that the algorithm provides more stable results, leading to visually consistent results for progressive rendering and making the approach applicable to streaming data. We show the applicability of our approach to spatio-temporal simulation ensemble data where we add the individual ensemble members progressively.