The Johnson-Lindenstrauss Lemma Is Optimal for Linear Dimensionality Reduction

Authors Kasper Green Larsen, Jelani Nelson

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Kasper Green Larsen
Jelani Nelson

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Kasper Green Larsen and Jelani Nelson. The Johnson-Lindenstrauss Lemma Is Optimal for Linear Dimensionality Reduction. In 43rd International Colloquium on Automata, Languages, and Programming (ICALP 2016). Leibniz International Proceedings in Informatics (LIPIcs), Volume 55, pp. 82:1-82:11, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2016)


For any n > 1, 0 < epsilon < 1/2, and N > n^C for some constant C > 0, we show the existence of an N-point subset X of l_2^n such that any linear map from X to l_2^m with distortion at most 1 + epsilon must have m = Omega(min{n, epsilon^{-2}*lg(N)). This improves a lower bound of Alon [Alon, Discre. Mathem., 1999], in the linear setting, by a lg(1/epsilon) factor. Our lower bound matches the upper bounds provided by the identity matrix and the Johnson-Lindenstrauss lemma [Johnson and Lindenstrauss, Contem. Mathem., 1984].
  • dimensionality reduction
  • lower bounds
  • Johnson-Lindenstrauss


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