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Visualizing High-Dimensional Graph Embeddings via Informed Multi-View Projections
Ya Ji, Xuefeng Li, Timo Brand +4
Graphs are commonly visualized in 2D, where humans readily interpret spatial relationships, yet such layouts often distort higher-dimensional structure. We propose to embed graphs…
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…
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…
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…
"Normalized Stress" is Not Normalized: How to Interpret Stress Correctly
Kiran Smelser, Jacob Miller, Stephen Kobourov
Stress is among the most commonly employed quality metrics and optimization criteria for dimension reduction projections of high dimensional data. Complex, high dimensional data is…
ENS-t-SNE: Embedding Neighborhoods Simultaneously t-SNE
Jacob Miller, Vahan Huroyan, Raymundo Navarrete +2
When visualizing a high-dimensional dataset, dimension reduction techniques are commonly employed which provide a single 2-dimensional view of the data. We describe ENS-t-SNE: an a…