PowerPoint Presentation - Dimension reduction in large data sets

PCA and EOFs

ĄPrincipal component analysis (Hotelling, 1933)

ĄEmpirical orthogonal functions (Lorenz, 1956)

ĄOther names too

ĄReduces dimensionality by finding linear combinations of a large set of variables that successively maximise variance

ĄLimitations

–Can be more difficult to interpret than using a subset of the original variables, but typically not for space-time data

– Linearity. Non-linear versions exist – not discussed here.

–Uses only covariances, not higher-order moments – see independent component analysis (ICA)