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A Sparse Johnson-Lindenstrauss Transform Using Fast Hashing

Authors Jakob Bæk Tejs Houen , Mikkel Thorup

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Author Details

Jakob Bæk Tejs Houen
  • BARC, Department of Computer Science, University of Copenhagen, Denmark
Mikkel Thorup
  • BARC, Department of Computer Science, University of Copenhagen, Denmark

Funding

Research supported by Investigator Grant 16582, Basic Algorithms Research Copenhagen (BARC), from the VILLUM Foundation.

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Jakob Bæk Tejs Houen and Mikkel Thorup. A Sparse Johnson-Lindenstrauss Transform Using Fast Hashing. In 50th International Colloquium on Automata, Languages, and Programming (ICALP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 261, pp. 76:1-76:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023) https://doi.org/10.4230/LIPIcs.ICALP.2023.76

Abstract

The Sparse Johnson-Lindenstrauss Transform of Kane and Nelson (SODA 2012) provides a linear dimensionality-reducing map A ∈ ℝ^{m × u} in 𝓁₂ that preserves distances up to distortion of 1 + ε with probability 1 - δ, where m = O(ε^{-2} log 1/δ) and each column of A has O(ε m) non-zero entries. The previous analyses of the Sparse Johnson-Lindenstrauss Transform all assumed access to a Ω(log 1/δ)-wise independent hash function. The main contribution of this paper is a more general analysis of the Sparse Johnson-Lindenstrauss Transform with less assumptions on the hash function. We also show that the Mixed Tabulation hash function of Dahlgaard, Knudsen, Rotenberg, and Thorup (FOCS 2015) satisfies the conditions of our analysis, thus giving us the first analysis of a Sparse Johnson-Lindenstrauss Transform that works with a practical hash function.

Subject Classification

ACM Subject Classification
  • Theory of computation → Random projections and metric embeddings
  • Theory of computation → Pseudorandomness and derandomization
Keywords
  • dimensionality reduction
  • hashing
  • concentration bounds
  • moment bounds

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