Scaling
Remark taught in Principal component analysis, part of Statistical learning
Scaling
| Type | Remark |
| Taught in | Principal component analysis |
| Part | Statistical learning |
Changing metres to millimetres multiplies a coordinate by 1000 and its variance by 10^6. Covariance principal components can therefore change even though the physical observations have not changed. Standardising each nonconstant coordinate to unit variance gives correlation principal components. This chooses equal weight per standardised coordinate, which may or may not suit the scientific question. It is a modelling choice. Record the choice, and reuse the training means and scales for new observations.
Used in
- Taught in Principal component analysis, where the derivation and examples are.
- All 7 questions on Principal component analysis
See also
Variance · Mean vector and covariance matrix · Correlation · Principal component directions and scores · Principal component directions · Principal components by Lagrange multipliers · Reconstruction from principal components · Choosing the number of principal components