Progressive Glimmer: Expanding Dimensionality in Multidimensional Scaling
Marina Evers, David H\"agele, S\"oren D\"oring, Daniel Weiskopf

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…
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Taxonomy
TopicsMusic Technology and Sound Studies · Design Education and Practice · Interactive and Immersive Displays
