Local Neighborhood Instability in Parametric Projections: Quantitative and Visual Analysis

arXiv cs.CV / 4/24/2026

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Key Points

The paper introduces a stability evaluation framework for parametric projections that tests how 2D embeddings deform under Gaussian noise and data drift around anchor points.
It quantifies local instability using measures such as mean displacement, bias, and nearest-anchor assignment error, and provides detailed visual diagnostics (displacement vectors, local PCA ellipsoids, and Voronoi misassignment).
The framework is validated on UMAP- and t-SNE-based neural projectors across different network sizes, and it analyzes how Jacobian regularization improves robustness.
Experiments on MNIST and Fashion-MNIST show the method can detect unstable regions that standard metrics like reconstruction error or neighborhood-preservation fail to reveal.

Abstract

Parametric projections let analysts embed new points in real time, but input variations from measurement noise or data drift can produce unpredictable shifts in the 2D layout. Whether and where a projection is locally stable remains largely unexamined. In this paper, we present a stability evaluation framework that probes parametric projections with Gaussian perturbations around selected anchor points and assesses how neighborhoods deform in the 2D embedding. Our approach combines quantitative measures of mean displacement, bias, and nearest-anchor assignment error with per-anchor visualizations of displacement vectors, local PCA ellipsoids, and Voronoi misassignment for detailed inspection. We demonstrate the framework's effectiveness on UMAP- and t-SNE-based neural projectors of varying network sizes and study the effect of Jacobian regularization as a gradient-based robustness strategy. We apply our framework to the MNIST and Fashion-MNIST datasets. The results show that our framework identifies unstable projection regions invisible to reconstruction error or neighborhood-preservation metrics.