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Principal Component Analysis as a book category groups works that explain, develop, and apply the statistical and computational technique of PCA and its close relatives for dimensionality reduction, data summarization, feature extraction and visualization. Books in this category range from introductory tutorials that teach the intuition, linear algebra (covariance, eigenvectors, SVD) and basic implementation in R/Python, to advanced texts on variants and theory (kernel PCA, sparse/robust PCA, probabilistic PCA, connections to factor analysis and manifold learning), as well as domain-specific casebooks applying PCA in fields like genetics, chemometrics, finance, image processing and signal analysis. Typical readers are data scientists, statisticians, engineers and applied researchers; titles commonly include worked examples, exercises, code, and guidance on preprocessing, interpretation, model selection and limitations (e.g., scaling, noise, linearity assumptions).

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