1 citations · 2 across the 8 of their papers we have counts for
3 papers · 1 filter
cardinalR: Generating Interesting High-Dimensional Data Structures
Jayani P. Gamage, Dianne Cook, Paul Harrison +2
Simulated high-dimensional data is useful for testing, validating, and improving algorithms used in dimension reduction, supervised and unsupervised learning. High-dimensional data…
quollr: An R Package for Visualizing 2-D Models from Nonlinear Dimension Reductions in High-Dimensional Space
Jayani P. Gamage, Dianne Cook, Paul Harrison +2
Nonlinear dimension reduction methods provide a low-dimensional representation of high-dimensional data by applying a Nonlinear transformation. However, the complexity of the trans…
Choosing Better NLDR Layouts by Evaluating the Model in the High-dimensional Data Space
Jayani P. Gamage, Dianne Cook, Paul Harrison +2
Nonlinear dimension reduction (NLDR) techniques such as tSNE, and UMAP provide a low-dimensional representation of high-dimensional data () by applying a nonlinear tran…