TY - GEN
T1 - Comparing and Exploring High-Dimensional Data with Dimensionality Reduction Algorithms and Matrix Visualizations
AU - Cutura, Rene
AU - Aupetit, Michaël
AU - Fekete, Jean Daniel
AU - Sedlmair, Michael
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/9/28
Y1 - 2020/9/28
N2 - We propose Compadre, a tool for visual analysis for comparing distances of high-dimensional (HD) data and their low-dimensional projections. At the heart is a matrix visualization to represent the discrepancy between distance matrices, linked side-by-side with 2D scatterplot projections of the data. Using different examples and datasets, we illustrate how this approach fosters (1) evaluating dimensionality reduction techniques w.r.t. how well they project the HD data, (2) comparing them to each other side-by-side, and (3) evaluate important data features through subspace comparison. We also present a case study, in which we analyze IEEE VIS authors from 1990 to 2018, and gain new insights on the relationships between coauthors, citations, and keywords. The coauthors are projected as accurately with UMAP as with t-SNE but the projections show different insights. The structure of the citation subspace is very different from the coauthor subspace. The keyword subspace is noisy yet consistent among the three IEEE VIS sub-conferences.
AB - We propose Compadre, a tool for visual analysis for comparing distances of high-dimensional (HD) data and their low-dimensional projections. At the heart is a matrix visualization to represent the discrepancy between distance matrices, linked side-by-side with 2D scatterplot projections of the data. Using different examples and datasets, we illustrate how this approach fosters (1) evaluating dimensionality reduction techniques w.r.t. how well they project the HD data, (2) comparing them to each other side-by-side, and (3) evaluate important data features through subspace comparison. We also present a case study, in which we analyze IEEE VIS authors from 1990 to 2018, and gain new insights on the relationships between coauthors, citations, and keywords. The coauthors are projected as accurately with UMAP as with t-SNE but the projections show different insights. The structure of the citation subspace is very different from the coauthor subspace. The keyword subspace is noisy yet consistent among the three IEEE VIS sub-conferences.
KW - Dimensionality Reduction
KW - High-Dimensional Data
KW - Matrix Visualization
KW - Subspace Analysis
KW - Visual Comparison
UR - http://www.scopus.com/inward/record.url?scp=85123040793&partnerID=8YFLogxK
U2 - 10.1145/3399715.3399875
DO - 10.1145/3399715.3399875
M3 - Conference contribution
AN - SCOPUS:85123040793
T3 - ACM International Conference Proceeding Series
BT - Proceedings of the Working Conference on Advanced Visual Interfaces, AVI 2020
A2 - Tortora, Genny
A2 - Vitiello, Giuliana
A2 - Winckler, Marco
PB - Association for Computing Machinery
T2 - 2020 International Conference on Advanced Visual Interfaces, AVI 2020
Y2 - 28 September 2020 through 2 October 2020
ER -