PCA (Principal Component Analysis)

What is PCA (Principal Component Analysis)?

PCA is a dimensionality reduction technique that transforms data into a new coordinate system, preserving as much variance (information) as possible with fewer variables.

Where did the term "PCA (Principal Component Analysis)" come from?

Invented by Karl Pearson in 1901.

How is "PCA (Principal Component Analysis)" used today?

Widely used for data visualization and noise reduction.

Related Terms

• Machine Learning
• Variance
• Feature Engineering