%0 Generic %1 gortler2022replication %A Görtler, Jochen %A Spinner, Thilo %A Weiskopf, Daniel %A Deussen, Oliver %D 2022 %K %R 10.18419/darus-2321 %T Replication Data for: Uncertainty-Aware Principal Component Analysis %X This dataset contains the source code for uncertainty-aware principal component analysis (UA-PCA) and a series of images that show dimensionality reduction plots created with UA-PCA. The software is a JavaScript library for performing principal component analysis and dimensionality reduction on datasets consisting of multivariate probability distributions.Each plot of the image series used UA-PCA to project a dataset consisting of multivariate normal distributions. The covariance matrices of the dataset instances were scaled with different factors resulting in different UA-PCA projections. The projected probability distributions are displayed using isolines of their probability density functions. As the scaling value increases, the projection changes, showing the sensitivity of UA-PCA to changes in variance.

@misc{gortler2022replication, abstract = {This dataset contains the source code for uncertainty-aware principal component analysis (UA-PCA) and a series of images that show dimensionality reduction plots created with UA-PCA. The software is a JavaScript library for performing principal component analysis and dimensionality reduction on datasets consisting of multivariate probability distributions.Each plot of the image series used UA-PCA to project a dataset consisting of multivariate normal distributions. The covariance matrices of the dataset instances were scaled with different factors resulting in different UA-PCA projections. The projected probability distributions are displayed using isolines of their probability density functions. As the scaling value increases, the projection changes, showing the sensitivity of UA-PCA to changes in variance. }, added-at = {2023-08-31T15:02:23.000+0200}, affiliation = {Görtler, Jochen/Universität Konstanz, Spinner, Thilo/Universität Konstanz, Weiskopf, Daniel/Universität Stuttgart, Deussen, Oliver/Universität Konstanz}, author = {Görtler, Jochen and Spinner, Thilo and Weiskopf, Daniel and Deussen, Oliver}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/269741f7ed65ae9eeee1c591c510b60d1/puma-wartung}, doi = {10.18419/darus-2321}, howpublished = {Software}, interhash = {5e042d551652a91e16cb97a658c02f2b}, intrahash = {69741f7ed65ae9eeee1c591c510b60d1}, keywords = {}, note = {Related to: J. Görtler, T. Spinner, D. Streeb, D. Weiskopf and O. Deussen, "Uncertainty-Aware Principal Component Analysis," in IEEE Transactions on Visualization and Computer Graphics, vol. 26, no. 1, pp. 822-831, Jan. 2020. doi: 10.1109/TVCG.2019.2934812}, orcid-numbers = {Görtler, Jochen/0000-0001-6510-0908, Spinner, Thilo/0000-0002-1168-1804, Weiskopf, Daniel/0000-0003-1174-1026, Deussen, Oliver/0000-0001-5803-2185}, timestamp = {2023-08-31T13:02:23.000+0200}, title = {Replication Data for: Uncertainty-Aware Principal Component Analysis}, year = 2022 }