AI RESEARCH
When One Point Is Not Enough: Addressing Ambiguous Instances in Dimensionality Reduction by Splitting
arXiv CS.LG
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ArXi:2605.23540v1 Announce Type: new Dimensionality Reduction (DR) methods are widely used to visualize high-dimensional data. One key task in DR-based analysis is discovering neighborhoods, which relies on analyzing the fine-grained local structure of a projection. However, DR is an inherently lossy process; no technique can perfectly preserve the high-dimensional relationships, and projections therefore contain visual artifacts. In this paper, we highlight a typically overlooked source of visual artifacts: ambiguous instances.