In analysis of galaxy-observation data, the term PCA analysis is used for a type of method of determining their morphology (galaxy classification) in an automatable manner. It makes use of principle component analysis (PCA), a general statistical method used to devise independent random variables that explain the variation. A variant, weighted principle component analysis (WPCA) is used in the galaxy classification methods, which incorporates methods of compensating for biases.
A principle curve (P-curve) is like a principle component, but is non-linear, rather than a straight line through the distribution, is a smooth curve through the data that captures desired qualities. A transformation must be devised for the purposes of the analysis, though some common techniques are established. PCA is often used in these techniques.
For galaxy classification, parameters associated with the observation of many galaxies are taken as a data set, and a principle curve is derived such that the transform to place a galaxy on the principle curve reveals its morphology.