BibTeX Export for Near-Neighbor Preserving Dimension Reduction for Doubling Subsets of l_1

Copy to Clipboard Download

@InProceedings{emiris_et_al:LIPIcs.APPROX-RANDOM.2019.47,
  author =	{Emiris, Ioannis Z. and Margonis, Vasilis and Psarros, Ioannis},
  title =	{{Near-Neighbor Preserving Dimension Reduction for Doubling Subsets of l\underline1}},
  booktitle =	{Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2019)},
  pages =	{47:1--47:13},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-125-2},
  ISSN =	{1868-8969},
  year =	{2019},
  volume =	{145},
  editor =	{Achlioptas, Dimitris and V\'{e}gh, L\'{a}szl\'{o} A.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX-RANDOM.2019.47},
  URN =		{urn:nbn:de:0030-drops-112628},
  doi =		{10.4230/LIPIcs.APPROX-RANDOM.2019.47},
  annote =	{Keywords: Approximate nearest neighbor, Manhattan metric, randomized embedding}
}

The metadata provided by Dagstuhl Publishing on its webpages, as well as their export formats (such as XML or BibTeX) available at our website, is released under the CC0 1.0 Public Domain Dedication license. That is, you are free to copy, distribute, use, modify, transform, build upon, and produce derived works from our data, even for commercial purposes, all without asking permission. Of course, we are always happy if you provide a link to us as the source of the data.

Read the full CC0 1.0 legal code for the exact terms that apply: https://creativecommons.org/publicdomain/zero/1.0/legalcode