Principal component analysis (PCA) is a classical machine learning technique. The goal of PCA is to transform a dataset into one with fewer columns. This is called dimensionality reduction. The ...
For PCA it doesn't help to overthink the deep mathematics of eigenvalues and eigenvectors. You can think of eigenvalues and eigenvectors as a kind of factorization of a matrix that captures all the ...
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