This chapter presents an introduction to some of the key dimensionality reduction methods currently used in data science. The intended audience is pure mathematicians. The chapter puts special emphasis on connecting the fundamental ideas in dimensionality reduction to a diverse set of mathematical domains, including geometry, dynamical systems, representation theory, and category theory.
Published: April 25, 2025
Citation
Chepushtanova S., E. Farnell, E. Kehoe, M. Kirby, and H.J. Kvinge. 2021.Dimensionality reduction. In Data Science for Mathematicians. Handbooks in Mathematics Series, edited by N. Carter. 7.1 - 7.11.2. New York, New York:Chapman and Hall/CRC.PNNL-SA-150925.doi:10.1201/9780429398292-7