| Title: |
Controlling the scatterplot shapes of 2D and 3D multidimensional projections |
| Authors: |
Machado, Alister; Telea, Alexandru; Behrisch, Michael; Sub Visualisation and Graphics; Visualisation and Graphics |
| Publication Year: |
2024 |
| Subject Terms: |
Data visualization; Dimensionality reduction; Projection; Software; Signal Processing; General Engineering; Human-Computer Interaction; Computer Vision and Pattern Recognition; Computer Graphics and Computer-Aided Design |
| Description: |
Multidimensional projections are effective techniques for depicting high-dimensional data. The point patterns created by such techniques, or a technique's visual signature, depend — apart from the data themselves — on the technique design and its parameter settings. Controlling such visual signatures — something that only few projections allow — can bring additional freedom for generating insightful depictions of the data. We present a novel projection technique — ShaRP — that allows explicit control on such visual signatures in terms of shapes of similar-value point clusters (settable to rectangles, triangles, ellipses, and convex polygons) and the projection space (2D or 3D Euclidean or S2). We show that ShaRP scales computationally well with dimensionality and dataset size, provides its signature-control by a small set of parameters, allows trading off projection quality to signature enforcement, and can be used to generate decision maps to explore the behavior of trained machine-learning classifiers. |
| Document Type: |
article in journal/newspaper |
| File Description: |
application/pdf |
| Language: |
English |
| ISSN: |
0097-8493 |
| Relation: |
https://dspace.library.uu.nl/handle/1874/473339 |
| Availability: |
https://dspace.library.uu.nl/handle/1874/473339 |
| Rights: |
info:eu-repo/semantics/OpenAccess |
| Accession Number: |
edsbas.C96B4AA2 |
| Database: |
BASE |