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  • 标题:A Spectral Gap Precludes Low-Dimensional Embeddings
  • 本地全文:下载
  • 作者:Assaf Naor
  • 期刊名称:LIPIcs : Leibniz International Proceedings in Informatics
  • 电子版ISSN:1868-8969
  • 出版年度:2017
  • 卷号:77
  • 页码:50:1-50:16
  • DOI:10.4230/LIPIcs.SoCG.2017.50
  • 出版社:Schloss Dagstuhl -- Leibniz-Zentrum fuer Informatik
  • 摘要:We prove that if an n-vertex O(1)-expander embeds with average distortion D into a finite dimensional normed space X, then necessarily the dimension of X is at least n^{c/D} for some universal constant c>0. This is sharp up to the value of the constant c, and it improves over the previously best-known estimate dim(X)> c(log n)^2/D^2 of Linial, London and Rabinovich, strengthens a theorem of Matousek, and answers a question of Andoni, Nikolov, Razenshteyn and Waingarten.
  • 关键词:Metric embeddings; dimensionality reduction; expander graphs; nonlinear spectral gaps; nearest neighbor search; complex interpolation; Markov type.
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