: Principal Component Analysis and Randomness Test for Big Data Analysis: Practical Applications of RMT-Based TechniqueАвтор
: Mieko Tanaka-Yamawaki, Yumihiko IkuraИздательство
: Springer Год
: 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.