Supplemental Material for Uncertainty-Aware Multidimensional Scaling
D. Hägele, T. Krake, and D. Weiskopf. Dataset, (2022)Related to: D. Hägele, T. Krake and D. Weiskopf, Üncertainty-Aware Multidimensional Scaling," in IEEE Transactions on Visualization and Computer Graphics, 2022. doi: 10.1109/TVCG.2022.3209420.
DOI: 10.18419/darus-3104
Abstract
This dataset contains the supplemental material for Üncertainty-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.
Related to: D. Hägele, T. Krake and D. Weiskopf, Üncertainty-Aware Multidimensional Scaling," in IEEE Transactions on Visualization and Computer Graphics, 2022. doi: 10.1109/TVCG.2022.3209420
%0 Generic
%1 hagele2022supplemental
%A Hägele, David
%A Krake, Tim
%A Weiskopf, Daniel
%D 2022
%K darus mult ubs_10005 ubs_10017 ubs_10018 ubs_20008 ubs_20024 ubs_30086 ubs_30200 ubs_40132 unibibliografie
%R 10.18419/darus-3104
%T Supplemental Material for Uncertainty-Aware Multidimensional Scaling
%X This dataset contains the supplemental material for Üncertainty-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.
@misc{hagele2022supplemental,
abstract = {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. },
added-at = {2022-10-18T08:40:05.000+0200},
affiliation = {Hägele, David/Universität Stuttgart, Krake, Tim/Universität Stuttgart, Weiskopf, Daniel/Universität Stuttgart},
author = {Hägele, David and Krake, Tim and Weiskopf, Daniel},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/21b1f3ed6bcbd80823ff334d356a27b45/unibiblio},
doi = {10.18419/darus-3104},
howpublished = {Dataset},
interhash = {4ec44595b67627502412e435c86d2159},
intrahash = {1b1f3ed6bcbd80823ff334d356a27b45},
keywords = {darus mult ubs_10005 ubs_10017 ubs_10018 ubs_20008 ubs_20024 ubs_30086 ubs_30200 ubs_40132 unibibliografie},
note = {Related to: D. Hägele, T. Krake and D. Weiskopf, "Uncertainty-Aware Multidimensional Scaling," in IEEE Transactions on Visualization and Computer Graphics, 2022. doi: 10.1109/TVCG.2022.3209420},
orcid-numbers = {Hägele, David/0000-0002-2679-6882, Weiskopf, Daniel/0000-0003-1174-1026},
timestamp = {2022-10-18T06:40:05.000+0200},
title = {Supplemental Material for Uncertainty-Aware Multidimensional Scaling},
year = 2022
}
