Source Code for Uncertainty-Aware Multidimensional Scaling

DOI

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.

Identifier
DOI https://doi.org/10.18419/darus-2995
Metadata Access https://darus.uni-stuttgart.de/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.18419/darus-2995
Provenance
Creator Hägele, David ORCID logo; Krake, Tim
Publisher DaRUS
Contributor Hägele, David; Weiskopf, Daniel
Publication Year 2022
Funding Reference DFG 251654672
Rights MIT License; info:eu-repo/semantics/openAccess; https://spdx.org/licenses/MIT.html
OpenAccess true
Contact Hägele, David (Universität Stuttgart); Weiskopf, Daniel (Universität Stuttgart)
Representation
Resource Type Dataset
Format application/java-archive; application/x-gzip
Size 1688250; 27893
Version 1.0
Discipline Other