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cs.LG2026
Bridging Graph Drawing and Dimensionality Reduction with Stochastic Stress Optimization
Daniel Hangan, Stephen Kobourov, Jacob Miller
Both Dimensionality Reduction (DR) and Graph Drawing (GD) aim to visualize abstract, non-linear structures, yet rely on different optimization paradigms. This contrast is evident i…
cs.LG2026
Class Angular Distortion Index for Dimensionality Reduction
Kaviru Gunaratne, Stephen Kobourov, Jacob Miller
Dimensionality reduction (DR) techniques are often characterized by whether they preserve global, high-level structures in the data or local, neighborhood structures. This distinct…
cs.LG2025
How Scale Breaks "Normalized Stress" and KL Divergence: Rethinking Quality Metrics
Kiran Smelser, Kaviru Gunaratne, Jacob Miller +1
Complex, high-dimensional data is ubiquitous across many scientific disciplines, including machine learning, biology, and the social sciences. One of the primary methods of visuali…