What is Principal Component Analysis (PCA)?

What is Principal Component Analysis (PCA)?

Approach: Describe the orthogonal transformation of correlated variables.

An unsupervised linear transformation technique used for dimensionality reduction. It identifies the directions (principal components) along which the variance in the data is maximal. It uses an orthogonal transformation to convert correlated variables into a set of linearly uncorrelated variables.

Follow-up: Is PCA sensitive to the scale of the features?

Key concepts: eigenvalues, variance, dimensionality reduction.