mlsanityData to 7 Oct 2026

Local versions of sum-of-norms clustering

arXiv 2109.09589 ↗First posted 20 Sep 2021cs.LG math.ST stat.MLcited by 4

Not on the map. Why?

Abstract (arXiv)

Sum-of-norms clustering is a convex optimization problem whose solution can be used for the clustering of multivariate data. We propose and study a localized version of this method, and show in particular that it can separate arbitrarily close balls in the stochastic ball model. More precisely, we prove a quantitative bound on the error incurred in the clustering of disjoint connected sets. Our bound is expressed in terms of the number of datapoints and the localization length of the functional.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 13 of its 33 references that are in the map. First posted on arXiv 20 Sep 2021.

Builds on

13 of 33 references.

References · 13

  1. 2013Splitting Methods for Convex Clusteringcited by 28
  2. 2018Convex Clustering: Model, Theoretical Guarantee and Efficient Algorithmcited by 26
  3. 2015Statistical Properties of Convex Clusteringcited by 19
  4. 2014Relax, no need to round: integrality of clustering formulationscited by 14
  5. 2018Recovering Trees with Convex Clusteringcited by 11
  6. 2019Recovery of a mixture of Gaussians by sum-of-norms clusteringcited by 7
  7. 2020The ratio-cut polytope and K-means clusteringcited by 7
  8. 2014Convex clustering via ℓ1 fusion penalizationcited by 6
  9. 2013Recovery guarantees for exemplar-based clusteringcited by 5
  10. 2020Certifying clusters from sum-of-norms clusteringcited by 3
  11. 2021Sum-of-norms clustering does not separate nearby ballscited by 3
  12. 2021On Convex Clustering Solutionscited by 3
1 more
  1. 2021K-median: exact recovery in the extended stochastic ball modelcited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 1; 2025: 0; 2026: 1.

4 papers.

  1. 2023Randomly Projected Convex Clustering Model: Motivation, Realization, and Cluster Recovery Guaranteescited by 1
  2. 2023Re-embedding data to strengthen recovery guarantees of clusteringcited by 0
  3. 2024Adaptive Self-supervised Robust Clustering for Unstructured Data with Unknown Cluster Numbercited by 0
  4. 2026Affinity Graph Connectivity in Convex Clusteringcited by 0