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  • 标题:Embedding Approximately Low-Dimensional l_2^2 Metrics into l_1
  • 本地全文:下载
  • 作者:Amit Deshpande ; Prahladh Harsha ; Rakesh Venkat
  • 期刊名称:LIPIcs : Leibniz International Proceedings in Informatics
  • 电子版ISSN:1868-8969
  • 出版年度:2016
  • 卷号:65
  • 页码:10:1-10:13
  • DOI:10.4230/LIPIcs.FSTTCS.2016.10
  • 出版社:Schloss Dagstuhl -- Leibniz-Zentrum fuer Informatik
  • 摘要:Goemans showed that any n points x_1,..., x_n in d-dimensions satisfying l_2^2 triangle inequalities can be embedded into l_{1}, with worst-case distortion at most sqrt{d}. We consider an extension of this theorem to the case when the points are approximately low-dimensional as opposed to exactly low-dimensional, and prove the following analogous theorem, albeit with average distortion guarantees: There exists an l_{2}^{2}-to-l_{1} embedding with average distortion at most the stable rank, sr(M), of the matrix M consisting of columns {x_i-x_j}_{i
  • 关键词:Metric Embeddings; Sparsest Cut; Negative type metrics; Approximation Algorithms
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