Publication Date:
2024-04-04
Description:
This book on Principal Component Analysis (PCA) extensively explores the core analyses and case studies within this field, incorporating the latest advancements. Each chapter delves into various disciplines like engineering, administration, economics, and technology, showcasing diverse applications and the utility of PCA. The book emphasizes the integration of PCA with other algorithms and methodologies, highlighting the enhancements achieved through combined approaches. Moreover, the book elucidates updated versions or iterations of PCA, detailing their descriptions and practical applications.
Keywords:
Eigenvectors
;
Eigenvalues
;
Covariance Matrix
;
Data Mining
;
Dimensionality Reduction Variance
;
Correlation
;
Principal Components
;
Data Compression
;
Scree Plot
;
Loadings
;
Algorithm
;
thema EDItEUR::P Mathematics and Science::PB Mathematics::PBK Calculus and mathematical analysis::PBKS Numerical analysis
Language:
English
Format:
image/jpeg
