APPLICATION OF THE EIGENVALUES AND EIGENVECTORS OF MATRICES WITH STATISTICAL DATA PROCESSING
Keywords:
eigenvalues, eigenvectors, covariance matrix, principal component analysis, multivariate statistical analysis, dimensionality reduction, correlation analysisAbstract
This article examines the application of eigenvalue and eigenvector theory within correlation analysis and principal component analysis for processing multivariate statistical data. Using the example of morphometric parameters of canine jaws and teeth, the authors describe in detail a step-by-step algorithm for data normalization, constructing a covariance matrix, and reducing the dimensionality of the feature space. Based on the obtained results, a meaningful interpretation of the first three principal components is proposed, enabling effective classification of the studied objects without significant loss of the original information.
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