An Introduction to Johnson-Lindenstrauss Transforms
It is an introductory overview for researchers and practitioners interested in dimensionality reduction techniques, but it is incremental as it does not present new findings.
This note explains Johnson-Lindenstrauss Transforms, which are tools for reducing data dimensionality while preserving key characteristics, and provides an overview of their use and development since the 1980s.
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.