A literature survey of matrix methods for data science. Issue 3 (10th September 2020)

• Record Type: Journal Article
• Authors: Stoll, Martin
• Other Names: Güttel Stefan guestEditor.; Liesen Jörg guestEditor.
• Is Part Of: Mitteilungen der Gesellschaft für Angewandte Mathematik und Mechanik. Volume 43:Issue 3(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-09-10
• Subjects: Data Science -- Numerical linear algebra; Mathematics -- Periodicals; Mechanics, Applied -- Periodicals; 510.5
• DOI: 10.1002/gamm.202000013
• Languages: English
• ISSNs: 0936-7195

Abstract

Abstract: Efficient numerical linear algebra is a core ingredient in many applications across almost all scientific and industrial disciplines. With this survey we want to illustrate that numerical linear algebra has played and is playing a crucial role in enabling and improving data science computations with many new developments being fueled by the availability of data and computing resources. We highlight the role of various different factorizations and the power of changing the representation of the data as well as discussing topics such as randomized algorithms, functions of matrices, and high‐dimensional problems. We briefly touch upon the role of techniques from numerical linear algebra used within deep learning.