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Principal Component Analysis (PCA)

  • Find a projection that captures the largest amount of variation in data
  • The original data are projected onto a much smaller space, resulting in dimensionality reduction. We find the eigenvectors of the covariance matrix, and these eigenvectors define the new space


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