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Found 4 papers, 2 papers with code
HUMAP: Hierarchical Uniform Manifold Approximation and Projection
Dimensionality reduction (DR) techniques help analysts understand patterns in high-dimensional spaces.
Model-agnostic interpretation by visualization of feature perturbations
Interpretation of machine learning models has become one of the most important research topics due to the necessity of maintaining control and avoiding bias in these algorithms.
Contrastive analysis for scatterplot-based representations of dimensionality reduction
We also introduce a bipartite graph to visually interpret and explore the relationship between the statistical variables employed to understand how the data features influence cluster formation.
A Grid-based Method for Removing Overlaps of Dimensionality Reduction Scatterplot Layouts
Dimensionality Reduction (DR) scatterplot layouts have become a ubiquitous visualization tool for analyzing multidimensional datasets.
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