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Universal consistency of the k-NN rule in metric spaces and Nagata dimension. II

arXiv 2305.17282 ↗First posted 26 May 2023cs.LGcited by 2

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Abstract (arXiv)

We continue to investigate the k nearest neighbour (k-NN) learning rule in complete separable metric spaces. Thanks to the results of C'erou and Guyader (2006) and Preiss (1983), this rule is known to be universally consistent in every such metric space that is sigma-finite dimensional in the sense of Nagata. Here we show that the rule is strongly universally consistent in such spaces in the absence of ties. Under the tie-breaking strategy applied by Devroye, Gy"orfi, Krzy.zak, and Lugosi (1994) in the Euclidean setting, we manage to show the strong universal consistency in non-Archimedian metric spaces (that is, those of Nagata dimension zero). Combining the theorem of C'erou and Guyader with results of Assouad and Quentin de Gromard (2006), one deduces that the k-NN rule is universally consistent in metric spaces having finite dimension in the sense of de Groot. In particular, the k-NN rule is universally consistent in the Heisenberg group which is not sigma-finite dimensional in the sense of Nagata as follows from an example independently constructed by Kor'anyi and Reimann (1995) and Sawyer and Wheeden (1992).

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 2 of its 29 references that are in the map. First posted on arXiv 26 May 2023.

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2 of 29 references.

References · 2

  1. 2020Universal consistency of the k-NN rule in metric spaces and Nagata dimensioncited by 4
  2. 2018Topics in Random Matrices and Statistical Machine Learningcited by 3

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Citing papers per year: 2025: 1; 2026: 1.

2 papers.