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  • 标题:Fast rates for empirical vector quantization
  • 本地全文:下载
  • 作者:Clément Levrard
  • 期刊名称:Electronic Journal of Statistics
  • 印刷版ISSN:1935-7524
  • 出版年度:2013
  • 卷号:7
  • 页码:1716-1746
  • DOI:10.1214/13-EJS822
  • 语种:English
  • 出版社:Institute of Mathematical Statistics
  • 摘要:We consider the rate of convergence of the expected loss of empirically optimal vector quantizers. Earlier results show that the mean-squared expected distortion for any fixed probability distribution supported on a bounded set and satisfying some regularity conditions decreases at the rate $\mathcal{O}(\log n/n)$. We prove that this rate is actually $\mathcal{O}(1/n)$. Although these conditions are hard to check, we show that well-clustered distributions with continuous densities supported on a bounded set are included in the scope of this result.
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