midPCA
What is covariance matrix in PCA?
Updated May 17, 2026
Short answer
It represents pairwise feature relationships used to compute principal components.
Deep explanation
Covariance matrix captures how features vary together. PCA uses it to identify directions of maximum variance by decomposing this matrix into eigenvalues and eigenvectors.
Real-world example
Understanding correlation in financial indicators.
Common mistakes
- Confusing covariance with correlation.
Follow-up questions
- What is diagonal of covariance matrix?
- Why transpose X?