Advances in Principal Component Analysis

Marquez, Fausto Pedro Garcia

Advances in Principal Component Analysis

Edited by Fausto Pedro Garcia Marquez

Abstract: This book describes and discusses the use of principal component analysis (PCA) for different types of problems in a variety of disciplines, including engineering, technology, economics, and more. It presents real-world case studies showing how PCA can be applied with other algorithms and methods to solve both large and small and static and dynamic problems. It also examines improvements made to PCA over the years.

JEL-codes: C10 (search for similar items in EconPapers)
Date: 2022
ISBN: 978-1-80355-765-6
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Chapters in this book:

Determining an Adequate Number of Principal Components: Stanley L. Sclove
Evaluation of Principal Component Analysis Variants to Assess Their Suitability for Mobile Malware Detection: Padmavathi Ganapathi, Roshni Arumugam and Shanmugapriya Dhathathri
Identification of Multilinear Systems: A Brief Overview: Laura-Maria Dogariu, Constantin Paleologu, Jacob Benesty and Silviu Ciochina
Mode Interpretation of Aerodynamic Characteristics of Tall Buildings Subject to Twisted Winds: Lei Zhou and Tim K.T. Tse
On the Use of Modified Winsorization with Graphical Diagnostic for Obtaining a Statistically Optimal Classification Accuracy in Predictive Discriminant Analysis: Augustine Iduseri
Prediction Analysis Based on Logistic Regression Modelling: Zaloa Sanchez-Varela
Principal Component Analysis and Artificial Intelligence Approaches for Solar Photovoltaic Power Forecasting: Souhaila Chahboun and Mohamed Maaroufi
Principal Component Analysis in Financial Data Science: Stefana Janicijevic, Vule Mizdrakovic and Maja Kljajic
Space-Time-Parameter PCA for Data-Driven Modeling with Application to Bioengineering: Florian De Vuyst, Claire Dupont and Anne-Virginie Salsac
Spatial Principal Component Analysis of Head-Related Transfer Functions and Its Domain Dependency: Shouichi Takane
The Foundation for Open Component Analysis: A System of Systems Hyper Framework Model: Ana Perisic and Branko Perisic
Variable Selection in Nonlinear Principal Component Analysis: Hiroko Katayama, Yuichi Mori and Masahiro Kuroda

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Persistent link: https://EconPapers.repec.org/RePEc:ito:pbooks:7471

DOI: 10.5772/intechopen.97992

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