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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.
The Foundation for Open Component Analysis: A System of Systems Hyper Framework Model
Identification of Multilinear Systems: A Brief Overview
Evaluation of Principal Component Analysis Variants to Assess Their Suitability for Mobile Malware Detection
Principal Component Analysis and Artificial Intelligence Approaches for Solar Photovoltaic Power Forecasting
Variable Selection in Nonlinear Principal Component Analysis
Space-Time-Parameter PCA for Data-Driven Modeling with Application to Bioengineering
Principal Component Analysis in Financial Data Science
Determining an Adequate Number of Principal Components
Spatial Principal Component Analysis of Head-Related Transfer Functions and Its Domain Dependency
Prediction Analysis Based on Logistic Regression Modelling
On the Use of Modified Winsorization with Graphical Diagnostic for Obtaining a Statistically Optimal Classification Accuracy in Predictive Discriminant Analysis
Mode Interpretation of Aerodynamic Characteristics of Tall Buildings Subject to Twisted Winds