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200 00$aAdvances in Principal Component Analysis /$fedited by Fausto Pedro Garci?a Ma?rquez
210  1$aLondon, United Kingdom :$cIntechOpen,$d2022.
215   $a1 online resource (252 pages) $cillustrations
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327   $a1. The Foundation for Open Component Analysis: A System of Systems Hyper Framework Model -- 2. Identification of Multilinear Systems: A Brief Overview -- 3. Evaluation of Principal Component Analysis Variants to Assess Their Suitability for Mobile Malware Detection -- 4. Principal Component Analysis and Artificial Intelligence Approaches for Solar Photovoltaic Power Forecasting -- 5. Variable Selection in Nonlinear Principal Component Analysis -- 6. Space-Time-Parameter PCA for Data-Driven Modeling with Application to Bioengineering -- 7. Principal Component Analysis in Financial Data Science  -- 8. Determining an Adequate Number of Principal Components -- 9. Spatial Principal Component Analysis of Head-Related Transfer Functions and Its Domain Dependency -- 10. Prediction Analysis Based on Logistic Regression Modelling -- 11. On the Use of Modified Winsorization with Graphical Diagnostic for Obtaining a Statistically Optimal Classification Accuracy in Predictive Discriminant Analysis -- 12. Mode Interpretation of Aerodynamic Characteristics of Tall Buildings Subject to Twisted Winds.
330   $aThis 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.
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