mlsanityData to 7 Oct 2026

Stability of sorting based embeddings

arXiv 2410.05446 ↗First posted 7 Oct 2024math.FA cs.LGcited by 3

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Abstract (arXiv)

Consider a group G of order M acting unitarily on a real inner product space V. We show that the sorting based embedding obtained by applying a general linear map α : ℝM × N → ℝD to the invariant map β_Phi : V → ℝM × N given by sorting the coorbits (langle v, g φi rangleV)g ∈ G, where (φi)i=1N ∈ V, satisfies a bi-Lipschitz condition if and only if it separates orbits. Additionally, we note that any invariant Lipschitz continuous map (into a Hilbert space) factors through the sorting based embedding, and that any invariant continuous map (into a locally convex space) factors through the sorting based embedding as well.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 10 of its 24 references that are in the map. First posted on arXiv 7 Oct 2024.

Builds on

10 of 24 references.

References · 10

  1. 2016Group Equivariant Convolutional Networkscited by 1,154
  2. 2018Why do deep convolutional networks generalize so poorly to small image transformations?cited by 198
  3. 2018Universal approximations of invariant maps by neural networkscited by 127
  4. 2021Scalars are universal: Equivariant machine learning, structured like classical physicscited by 127
  5. 2022Low Dimensional Invariant Embeddings for Universal Geometric Learningcited by 38
  6. 2022Permutation Invariant Representations with Applications to Graph Deep Learningcited by 20
  7. 2022Group-invariant max filteringcited by 18
  8. 2022Machine learning and invariant theorycited by 13
  9. 2022Group invariant machine learning by fundamental domain projectionscited by 11
  10. 2013Invertibility and Robustness of Phaseless Reconstructioncited by 3

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Citing papers per year: 2024: 1; 2025: 2.

3 papers.