5 citations · 5 across the 4 of their papers we have counts for
4 papers
ShapDBM: Exploring Decision Boundary Maps in Shapley Space
Luke Watkin, Daniel Archambault, Alex Telea
Decision Boundary Maps (DBMs) are an effective tool for visualising machine learning classification boundaries. Yet, DBM quality strongly depends on the dimensionality reduction (D…
LCIP: Loss-Controlled Inverse Projection of High-Dimensional Image Data
Yu Wang, Frederik L. Dennig, Michael Behrisch +1
Projections (or dimensionality reduction) methods aim to map high-dimensional data to typically 2D scatterplots for visual exploration. Inverse projection methods aim…
Visual Cluster Separation Using High-Dimensional Sharpened Dimensionality Reduction
Youngjoo Kim, Alexandru C. Telea, Scott C. Trager +1
Applying dimensionality reduction (DR) to large, high-dimensional data sets can be challenging when distinguishing the underlying high-dimensional data clusters in a 2D projection…
Quantitative Evaluation of Time-Dependent Multidimensional Projection Techniques
E. F. Vernier, R. Garcia, I. P. da Silva +2
Dimensionality reduction methods are an essential tool for multidimensional data analysis, and many interesting processes can be studied as time-dependent multivariate datasets. Th…