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Advances in principal component analysis / / edited by Fausto Pedro García Márquez



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Titolo: Advances in principal component analysis / / edited by Fausto Pedro García Márquez Visualizza cluster
Pubblicazione: London, England : , : IntechOpen, , 2022
Descrizione fisica: 1 online resource (252 pages)
Disciplina: 519.5354
Soggetto topico: Principal components analysis
Correspondence analysis (Statistics)
Persona (resp. second.): García MárquezFausto Pedro
Nota di contenuto: 1. 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.
Sommario/riassunto: 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.
Titolo autorizzato: Advances in Principal Component Analysis  Visualizza cluster
Formato: Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione: Inglese
Record Nr.: 9910688391503321
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