Feature space dimensionality reduction refers to the process of reducing the number of input variables in a dataset while preserving important information. This is crucial in text processing and normalization because it helps to simplify models, reduce overfitting, and improve computational efficiency. Techniques like Principal Component Analysis (PCA) or Singular Value Decomposition (SVD) are often employed to transform high-dimensional data into lower-dimensional representations without losing significant structure or meaning.
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