Úvod do analýzy hlavních komponent

Abstract

This thesis deals with the theoretical description and practical application of principal component analysis, which is one of the methods of multidimensional statistics used for data dimensionality reduction. The theoretical section summarizes concepts from linear algebra, particularly the topics of eigenvalues and eigenvectors, spectral decomposition, and singular value decomposition, which form the computational basis of the PCA method. The practical section focuses on the application of PCA in the Python programming language on real biomedical data from the field of neurointensive care.