Which statistical technique uses the SVD or Eigen-decomposition to reduce the dimensionality of data while preserving variance?
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Principal Component Analysis (PCA)
PCA identifies the 'principal components' (eigenvectors of the covariance matrix) that account for the most variance in the dataset. This allows for data compression and visualization of high-dimensional data.
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- Which dimensionality reduction technique relies on SVD or Eigen-decomposition to compress data while retaining as much variance as possible?