Eigenvalue Interpretation in PCA
In Principal Component Analysis (PCA), what do the eigenvalues of the covariance matrix represent?
Suppose you have a 100-dimensional dataset. The first three eigenvalues of the covariance matrix are $50$, $30$, and
0$, and the remaining 97 eigenvalues sum to
0$. How much variance is captured by the first 3 principal components?
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