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AIPrincipal Component Analysis (PCA) is a dimensionality reduction technique that transforms a dataset into a new coordinate system where the axes (principal components) capture the most variance (which has the most amount of detail/information).
The computation behind PCA involves calculating the covariance matrix of the data, followed by an eigenvalue decomposition.
The eigenvalues represent the amount of variance captured by each principal component, while the corresponding eigenvectors define the directions of these components.
Sorting the eigenvalues in descending order allows for selecting the most significant components, reducing dimensionality while keeping the most critical information.
C: deepia
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