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Dimensionality Reduction

Dimensionality Reduction

Process of finding a low-dimensional representation of higher-dimensional data that retains as much important information from the data as possible.

Dimensionality reduction, or dimension reduction, is a general process of projecting a set of high-dimensional vectors to a lower-dimensionality space while retaining metrics among them. In other words, dimensionality reduction aims to downsize high-dimensional data so that it can be represented in low-dimensional space without losing important information from the data.

There are several reasons why dimensionality reduction can be useful:

  • Data visualization - It's difficult or even impossible for humans to visualize high-dimensional data. Dimensionality reduction can represent that high-dimensional data in 2D or 3D.
  • Data compression - Storage space and computing power are costly resources. Dimensionality reduction makes data more efficient to store and easier to retrieve.
  • Noise removal - Data can often be corrupted or distorted to the point that it's difficult/impossible to understand and interpret it. Dimensionality reduction can reduce noise in data and have a positive effect on query accuracy.
Dimensionality Reduction Techniques

Numerous techniques of data mining and machine learning can be categorized as processes of dimensionality reduction.


Further Resources


Comprehensive Guide to 12 Dimensionality Reduction Techniques

Pulkit Sharma


Dimensionality Reduction - The Math of Intelligence #5

Siraj Raval

Dimensionality Reduction For Dummies -- Part 1: Intuition

Hussein Abdullatif



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