What is principal component analysis?

What is principal component analysis?

Approach: Describe orthogonal transformation to uncorrelated components.

Linear transformation of data to new coordinate system such that greatest variance lies on first coordinate (first principal component), second greatest on second, etc. Computed via eigen decomposition of covariance matrix.

Follow-up: How many components to keep?

Key concepts: eigen decomposition, variance maximization, dimensionality reduction.