juniorDimensionality Reduction
What is a covariance matrix in PCA?
Updated May 16, 2026
Short answer
A covariance matrix captures relationships between features in PCA.
Deep explanation
It shows how variables vary together. PCA uses eigenvectors of the covariance matrix to find principal components.
Real-world example
Understanding how stock prices move together in finance.
Common mistakes
- Ignoring scaling before computing covariance.
Follow-up questions
- What does positive covariance mean?
- Why is covariance important in PCA?