mlsanityData to 14 Oct 2026

On Hamming-Lipschitz Type Stability of the Subdominant (Minmax) Ultrametric: Theory and Simple Proofs

arXiv 2608.04014 ↗First posted 27 Apr 2026cs.LG cs.AIcited by 0

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Abstract (arXiv)

The subdominant (minmax) ultrametric is a canonical tree-structured summary of a dissimilarity matrix, arising equivalently as the ultrametric induced by single-linkage clustering. While its classical stability theory is usually formulated in ℓ_∞ or Gromov--Hausdorff terms, such bounds are poorly suited to sparse perturbations that alter only a few pairwise distances. We develop an ℓ0-type stability theory for this operator. Our analysis shows that sparse edits propagate only through the minimum spanning tree (MST): a pairwise ultrametric value can change only if its tree path crosses an edited edge or a cut newly exposed by an edited off-tree edge. This yields a sharp per-edit exposed-cut score and a tree-only global envelope, leading to Hamming--Lipschitz bounds on the number of ultrametric entries that can change. We also prove sharpness results showing that this dependence on tree geometry is unavoidable: under strict cut separation the tree-edge bound is attained exactly, and for off-tree edits there are explicit families in which one edited distance changes Theta(n2) ultrametric entries. In addition, we prove a conditional near-additivity principle for multiple edits under certified large per-edit changed regions and negligible aggregate overlap. Experiments on deep-embedding graphs show that the resulting structural scores provide useful vulnerability diagnostics for hierarchical representations.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 33 references that are in the map. First posted on arXiv 27 Apr 2026.

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14 of 33 references.

References · 14

  1. 2019Ultrametric Fitting by Gradient Descentcited by 12
  2. 2024View-Consistent Hierarchical 3D Segmentation Using Ultrametric Feature Fieldscited by 8
  3. 2025v-PuNNs: van der Put Neural Networks for Transparent Ultrametric Representation Learningcited by 5
  4. 2020Stable and consistent density-based clustering via multiparameter persistencecited by 3
  5. 2022Learning Ultrametric Trees for Optimal Transport Regressioncited by 3
  6. 2017Temporal Hierarchical Clusteringcited by 2
  7. 2023Engineering Massively Parallel MST Algorithmscited by 2
  8. 2023Convergence to Lexicographically Optimal Base in a (Contra)Polymatroid and Applications to Densest Subgraph and Tree Packingcited by 2
  9. 2013Gromov-Hausdorff stability of linkage-based hierarchical clustering methodscited by 1
  10. 2015Statistical Properties of the Single Linkage Hierarchical Clustering Estimatorcited by 1
  11. 2021Component Tree Loss Function: Definition and Optimizationcited by 1
  12. 2024Massively Parallel Minimum Spanning Tree in General Metric Spacescited by 1
2 more
  1. 2025Hierarchical clustering with maximum density paths and mixture modelscited by 1
  2. 2025Dynamic Matroids: Base Packing and Coveringcited by 1

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