%0 Generic %1 hagele2022source %A Hägele, David %A Krake, Tim %D 2022 %K %R 10.18419/darus-2995 %T Source Code for Uncertainty-Aware Multidimensional Scaling %X This dataset contains the source code for the uncertainty-aware multidimensional scaling (UAMDS) algorithm implemented in the Java programming language.UAMDS is a nonlinear dimensionality reduction technique for sets of random vectors. The implemented UAMDS model allows to project a set of multivariate normal distributions to low-dimensional space, e.g. 1D, 2D or 3D for visualization.This dataset only contains the core components for performing UAMDS without any accompanying visualization techniques.

@misc{hagele2022source, abstract = {This dataset contains the source code for the uncertainty-aware multidimensional scaling (UAMDS) algorithm implemented in the Java programming language.UAMDS is a nonlinear dimensionality reduction technique for sets of random vectors. The implemented UAMDS model allows to project a set of multivariate normal distributions to low-dimensional space, e.g. 1D, 2D or 3D for visualization.This dataset only contains the core components for performing UAMDS without any accompanying visualization techniques. }, added-at = {2025-03-10T11:22:21.000+0100}, affiliation = {Hägele, David/University of Stuttgart, Krake, Tim/University of Stuttgart}, author = {Hägele, David and Krake, Tim}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2c4c61dd837499e726684596757477366/unibiblio}, doi = {10.18419/darus-2995}, howpublished = {Software}, interhash = {a43bd8cc4657a47b79bde40c7c5f14b7}, intrahash = {c4c61dd837499e726684596757477366}, keywords = {}, note = {Related to: D. Hägele, T. Krake and D. Weiskopf, "Uncertainty-Aware Multidimensional Scaling," in IEEE Transactions on Visualization and Computer Graphics, vol. 29, no. 1, pp. 23-32, 2022. doi: 10.1109/TVCG.2022.3209420}, orcid-numbers = {Hägele, David/0000-0002-2679-6882, Krake, Tim/0009-0004-7084-3633}, timestamp = {2025-03-10T10:22:21.000+0100}, title = {Source Code for Uncertainty-Aware Multidimensional Scaling}, year = 2022 }