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        <datestamp>2024-03-06T10:41:49Z</datestamp>
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          <dc:title>On Using Toeplitz and Circulant Matrices for Johnson-Lindenstrauss Transforms</dc:title>
          <dc:creator>Freksen, Casper Benjamin</dc:creator>
          <dc:creator>Larsen, Kasper Green</dc:creator>
          <dc:subject>dimensionality reduction</dc:subject>
          <dc:subject>Johnson-Lindenstrauss</dc:subject>
          <dc:subject>Toeplitz matrices</dc:subject>
          <dc:description>The Johnson-Lindenstrauss lemma is one of the corner stone results in dimensionality reduction. It says that given N, for any set of N,&#13;
vectors X \subset R^n, there exists a mapping f : X --&gt; R^m such that f(X) preserves all pairwise distances between vectors in X to within(1 ± \eps) if m = O(\eps^{-2} lg N). Much effort has gone into developing&#13;
fast embedding algorithms, with the Fast Johnson-Lindenstrauss&#13;
transform of Ailon and Chazelle being one of the most well-known&#13;
techniques. The current fastest algorithm that yields the optimal m =&#13;
O(\eps{-2}lg N) dimensions has an embedding time of O(n lg n + \eps^{-2} lg^3 N). An exciting approach towards improving this, due to Hinrichs and Vybíral, is to use a random m times n Toeplitz matrix for the&#13;
embedding. Using Fast Fourier Transform, the embedding of a vector can&#13;
then be computed in O(n lg m) time. The big question is of course&#13;
whether m = O(\eps^{-2} lg N) dimensions suffice for this technique. If&#13;
so, this would end a decades long quest to obtain faster and faster&#13;
Johnson-Lindenstrauss transforms. The current best analysis of the&#13;
embedding of Hinrichs and Vybíral shows that m = O(\eps^{-2} lg^2 N)&#13;
dimensions suffice. The main result of this paper, is a proof that&#13;
this analysis unfortunately cannot be tightened any further, i.e.,&#13;
there exists a set of N vectors requiring m = \Omega(\eps^{-2} lg^2 N)&#13;
for the Toeplitz approach to work.</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Casper Benjamin Freksen and Kasper Green Larsen</dc:contributor>
          <dc:date>2017</dc:date>
          <dc:relation>Is Part Of LIPIcs, Volume 92, 28th International Symposium on Algorithms and Computation (ISAAC 2017)</dc:relation>
          <dc:type>InProceedings</dc:type>
          <dc:type>Text</dc:type>
          <dc:type>doc-type:ResearchArticle</dc:type>
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          <dc:identifier>doi:10.4230/LIPIcs.ISAAC.2017.32</dc:identifier>
          <dc:identifier>urn:nbn:de:0030-drops-82540</dc:identifier>
          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ISAAC.2017.32</dc:identifier>
          <dc:language>eng</dc:language>
          <dc:rights>https://creativecommons.org/licenses/by/3.0/legalcode</dc:rights>
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