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.

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

- Non-negative matrix factorization (NMF)
- Principal component analysis (PCA)
- Kernel PCA
- Independent component analysis (ICA)
- Nonlinear dimensionality reduction (NDR)
- Linear discriminant analysis (LDA)
- Factor analysis
- Many others

### Timeline

### Further Resources

Comprehensive Guide to 12 Dimensionality Reduction Techniques

Pulkit Sharma

Web

Dimensionality Reduction - The Math of Intelligence #5

Siraj Raval

Dimensionality Reduction For Dummies -- Part 1: Intuition

Hussein Abdullatif

Web