Supplemental Material for Uncertainty-Aware Multidimensional Scaling

DOI

This dataset contains the supplemental material for "Uncertainty-Aware Multidimensional Scaling". Uncertainty-aware multidimensional scaling (UAMDS) is a nonlinear dimensionality reduction technique for sets of random vectors.

This dataset consists of a PDF document that contains a detailed mathematical derivation for the normal distribution UAMDS algorithm, and additional visualizations.

Identifier
DOI https://doi.org/10.18419/darus-3104
Related Identifier IsCitedBy https://doi.org/10.1109/TVCG.2022.3209420
Metadata Access https://darus.uni-stuttgart.de/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.18419/darus-3104
Provenance
Creator Hägele, David ORCID logo; Krake, Tim; Weiskopf, Daniel ORCID logo
Publisher DaRUS
Contributor Hägele, David; Krake, Tim; Weiskopf, Daniel
Publication Year 2022
Funding Reference DFG 251654672
Rights CC BY 4.0; info:eu-repo/semantics/openAccess; http://creativecommons.org/licenses/by/4.0
OpenAccess true
Contact Hägele, David (Universität Stuttgart); Krake, Tim (Universität Stuttgart); Weiskopf, Daniel (Universität Stuttgart)
Representation
Resource Type Dataset
Format application/gzip; application/pdf
Size 16515064; 7394869
Version 1.0
Discipline Other