Name
Title | ||
|---|---|---|
EvoSets: Tracking the Sensitivity of Dimensionality Reduction Results Across Subspaces |
Abstract | ||
|---|---|---|
Dimensionality reduction is commonly used for identifying and analyzing patterns in the visual analysis of multi-dimensional datasets. The selection of subspaces is a core building block in projecting high-dimensional data to low-dimensional space, which is usually illustrated as a scatterplot for analysts to easily understand and explore. This process involves human prior knowledge and domain-specific requirements. Thus, quantifying and tracking the changes of dimensionality reduction results across subspaces remain challenging. Existing methods can neither quantify the subsets-based changes of dimensionality reduction results when switching subspaces, nor automatically and comprehensively display the overall and subtle differences among dimensionality reduction results. To address this, we developed
<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EvoSets</i>
, a novel visual analytics system designed to help users understand how subspaces affect dimensionality reduction results. The effects are quantified based on the distribution of subsets within projections to tracking the sensitivity of dimensionality reduction results across subspaces. In addition, the system supports the exploration of the overall evolution of the dimensionality reduction results for helping users track the convergence and divergence behavior changes of subsets based on an extended
<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Bubble Sets</i>
visualization. Similarities are intuitively illustrated, and dissimilarities are highlighted among the generated dimensionality reduction results across subspaces based on different layout constraints. The usefulness and effectiveness of the system are further evaluated with a user study and two case studies on multi-dimensional datasets. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TBDATA.2021.3079200 | IEEE Transactions on Big Data |
Keywords | DocType | Volume |
Subspace,dimensionality reduction result,convergence and divergence behavior changes,comparison | Journal | 8 |
Issue | ISSN | Citations |
6 | 2332-7790 | 0 |
PageRank | References | Authors |
0.34 | 32 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guo-Dao Sun | 1 | 171 | 11.24 |
| Sujia Zhu | 2 | 0 | 0.34 |
| Qi Jiang | 3 | 0 | 0.34 |
| Wang Xia | 4 | 0 | 0.34 |
| Ronghua Liang | 5 | 376 | 42.60 |