AI RESEARCH
Bridging Graph Drawing and Dimensionality Reduction with Stochastic Stress Optimization
arXiv CS.LG
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ArXi:2605.00641v1 Announce Type: new 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 in Multidimensional Scaling (MDS), which typically depends on the SMACOF algorithm despite graph drawing results showing that simpler stochastic optimization schemes can be effective for the same objective. We bridge these domains by adapting Stochastic Gradient Descent (SGD) techniques from graph drawing to vector data embedding.