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 ...
The transformed data is the matrix product of the normalized data and the eigenvectors. Because the eigenvectors were converted to rows for easier viewing, they must be passed as columns using the ...
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