  Archetypal analysis represents each individual member of a set of data
vectors as a mixture (a constrained linear combination) of the pure types or
archetypes of the data set. The archetypes are themselves required to be
mixtures of the data vectors. Archetypal analysis may be particularly useful in
analysing data sets comprising galaxy spectra, since each spectrum is,
presumably, a superposition of the emission from the various stellar
populations, nebular emissions and nuclear activity making up that galaxy, and
each of these emission sources corresponds to a potential archetype of the
entire data set. We demonstrate archetypal analysis using sets of composite
synthetic galaxy spectra, showing that the method promises to be an effective
and efficient way to classify spectra. We show that archetypal analysis is
robust in the presence of various types of noise.
