An Introduction to Johnson-Lindenstrauss Transforms
Data Structures and Algorithms
2021-03-02 v1 Machine Learning
Abstract
Johnson--Lindenstrauss Transforms are powerful tools for reducing the dimensionality of data while preserving key characteristics of that data, and they have found use in many fields from machine learning to differential privacy and more. This note explains what they are; it gives an overview of their use and their development since they were introduced in the 1980s; and it provides many references should the reader wish to explore these topics more deeply.
Cite
@article{arxiv.2103.00564,
title = {An Introduction to Johnson-Lindenstrauss Transforms},
author = {Casper Benjamin Freksen},
journal= {arXiv preprint arXiv:2103.00564},
year = {2021}
}
Comments
The text was previously a main part of the introduction of my PhD thesis, but it has been adapted to be self contained and serve as a (hopefully good) starting point for readers interested in the topic