A literature survey of matrix methods for data science
This is an incremental survey that synthesizes existing knowledge on matrix methods for data science practitioners and researchers.
The paper surveys the role of numerical linear algebra in data science, highlighting how factorizations, representation changes, and techniques like randomized algorithms have enabled and improved computations across various applications.
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.