Algorithmintro
Principal component analysis
Also called Hotelling transform, Karhunen-Loeve transform, PCA, proper orthogonal decomposition.
Project data onto the orthogonal directions of maximal variance, computed from the covariance eigenvectors or the SVD.
Project data onto the orthogonal directions of maximal variance, computed from the covariance eigenvectors or the SVD.
About this page
The build computes these levels from the evidence in the repository. Each row says what the next level needs. Read what the levels mean.
| Ladder | Level | What the next level needs |
|---|---|---|
| Human proof | H0 no proof |
|
| Lean | L0 no Lean part |
|
| Visual | V0 none |
|
| Writing | W0 stub |
|
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