Glossary · Phase 2 · Math, when you need it
Covariance matrix
A square table holding the covariance of every pair of features, with each feature's variance on the diagonal. PCA's directions are its eigenvectors.
Try it now
Turn the line to find the direction that keeps the most spread. That's PCA's first component.
Learn it properly
SVD and PCA · Compress data to its most important directions. About 30 minutes.
Other terms from the same lesson
- PCA: A way to boil many columns of data down to the few directions where the data varies most.
- principal component: One of the hidden directions PCA finds: a recipe that mixes the original columns. The first one captures the most spread in the data.
- singular value: A number from SVD that says how much the matrix stretches along one direction. Bigger means that direction matters more.
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