Название: Principal Component Analysis and Randomness Test for Big Data Analysis: Practical Applications of RMT-Based Technique
Автор: Mieko Tanaka-Yamawaki, Yumihiko Ikura
Издательство: Springer
Год: 2023
Страниц: 153
Язык: английский
Формат: pdf (true)
Размер: 10.2 MB

This book presents the novel approach of analyzing large-sized rectangular-shaped numerical data (so-called Big Data). The essence of this approach is to grasp the "meaning" of the data instantly, without getting into the details of individual data. Unlike conventional approaches of principal component analysis, randomness tests, and visualization methods, the authors' approach has the benefits of universality and simplicity of data analysis, regardless of data types, structures, or specific field of science.

This book was written to demonstrate the concept and usefulness of random matrix theory (RMT) in Big Data analysis, with emphasis on two RMT-oriented methodologies, RMT-PCA and RMT-test. Both are algorithms used in high-speed computer works. The book provides a thorough explanation of the methodologies and the output of real data, along with examples of programs and their outputs. Care is taken to convey the details of the methodology to potential readers who wish to analyze big data; to extract principal components using RMT-PCA; or to classify data by measuring the degree of randomness using RMT-test.






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