The process of reducing the dimensions of a dataset while preserving as much variance as possible is known as ________.

  • Principal Component Analysis
  • Random Sampling
  • Mean Shift
  • Agglomerative Clustering
Dimensionality reduction techniques like Principal Component Analysis (PCA) are used to reduce the dataset's dimensions while preserving variance. PCA identifies new axes (principal components) in the data to reduce dimensionality. Hence, "Principal Component Analysis" is the correct answer.
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