StatisticsML Knowledge
What’s your understanding of PCA and its inherent disadvantages?
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Answers
Anonymous
a year ago
PCA, or principal component analysis, is a process to determine what are the most important features. I like to think about it in terms of eigenvalues. If you have a diagonal matrix, then the eigenvalues are the diagonal terms, and the eigenvectors are the basis vectors. In PCA, the principal components and their effects aren't as visible, but it is a process to determine them. If I recall correctly, PCA will return the absolute value of the eigenvalues, rather than the eigenvalue itself.
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