Dimensional reduction is a process used in data analysis and machine learning to reduce the number of random variables or features in a dataset while preserving its essential information. This is particularly useful when dealing with high-dimensional data, which can be challenging to visualize, analyze, and model due to the "curse of dimensionality" — a phenomenon where the feature space becomes increasingly sparse and less manageable as the number of dimensions increases.
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Dimensional reduction retains fields independent of chosen internal coordinates. In a flat-torus zero-mode reduction, a metric supplies a metric, internal-component vectors and internal metric scalars; a massless p-form gauge field supplies lower-rank fields with internal indices. Fluxes, twists or projections can alter the spectrum and preserved supercharges.