Appendices

Max Garzon (), Lih-Yuan Deng (), Nirman Kumar (), Deepak Venugopal (), Kalidas Jana () and Ching-Chi Yang ()
Additional contact information
Max Garzon: The University of Memphis, Computer Science
Lih-Yuan Deng: The University of Memphis, Mathematical Sciences
Nirman Kumar: The University of Memphis, Computer Science
Deepak Venugopal: The University of Memphis, Computer Science
Kalidas Jana: Fogelman College of Business
Ching-Chi Yang: The University of Memphis, Mathematical Sciences

Chapter Chapter 11 in Dimensionality Reduction in Data Science, 2022, pp 219-265 from Springer

Abstract: Abstract This chapter presents a summary review of prerequisite concepts from statistics, mathematics and computer science, although readers are expected to have a nodding familiarity with most of them. It also provides some background on a number of computational problems and data sets used in the book or particularly useful for data science and dimensionality reduction; as well as a review of computing environments and platforms that could be used as a playground to run and test the methods and solutions described in this book. The aim is to provide a refresher of what they are and point to sources in the literature where they could be studied in more detail, if needed.

Date: 2022
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DOI: 10.1007/978-3-031-05371-9_11

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