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MENTAL MODEL · M11635

Dimensionality Reduction

Dimensionality Reduction
TechnicalHigh supportData Science
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Version 1.0.0 · Updated 2026-07-28

CORE DEFINITION

Dimensionality reduction, or dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the low-dimensional representation retains some meaningful properties of the original data, ideally close to its intrinsic dimension. Working in high-dimensional spaces can be undesirable for many reasons; raw data are often sparse as a consequence of the curse of dimensionality, and the computational cost of processing high-dimensional data can be prohibitive.

SCAFFOLDING EFFECT

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Reduce cognitive load

Dimensionality reduction, or dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the low-dimensional representation retains some meaningful properties of the original data, ideally close to its intrinsic dimension. Working in high-dimensional spaces can be undesirable for many reasons; raw data are often sparse as a consequence of the curse of dimensionality, and the computational cost of processing high-dimensional data can be prohibitive.

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Reduce dimensionality while preserving information as much as possible, thereby reducing noise and computational load.

MINIMUM ACTION

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Source support: Explicit

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    en.wikipedia.orghttps://en.wikipedia.org/wiki/Dimensionality_reductionZH · Explicit
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