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AI • Machine Learning • Tech on Instagram: "PCA begins by centering the data so each feature has zero mean, which ensures variance is measured correctly. The algorithm then computes the covariance matrix of the data to capture how features vary together. By performing an eigenvalue decomposition of this covariance matrix or an SVD of the data matrix, PCA computes eigenvectors that define the principal "directions" and eigenvalues that quantify how much variance each direction contributes. The da
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AI • Machine Learning • Tech on Instagram: "PCA begins by centering the data so each feature has zero mean, which ensures variance is measured correctly. The algorithm then computes the covariance matrix of the data to capture how features vary together. By performing an eigenvalue decomposition of this covariance matrix or an SVD of the data matrix, PCA computes eigenvectors that define the principal "directions" and eigenvalues that quantify how much variance each direction contributes. The da
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