Unsupervised classification of SDSS galaxy spectra
  Defining templates of galaxy spectra is useful to quickly characterise new
observations and organise databases from surveys. These templates are usually
built from a pre-defined classification based on other criteria. Aims. We
present an unsupervised classification of 702248 spectra of galaxies and
quasars with redshifts smaller than 0.25 that were retrieved from the Sloan
Digital Sky Survey (SDSS) database, release 7. The spectra were first corrected
for redshift, then wavelet-filtered to reduce the noise, and finally binned to
obtain about 1437 wavelengths per spectrum. The unsupervised clustering
algorithm Fisher-EM, relying on a discriminative latent mixture model, was
applied on these corrected spectra. The full set and several subsets of 100000
and 300000 spectra were analysed. The optimum number of classes given by a
penalised likelihood criterion is 86 classes, of which the 37 most populated
gather 99% of the sample. These classes are established from a subset of 302214
spectra. Using several cross-validation techniques we find that this
classification agrees with the results obtained on the other subsets with an
average misclassification error of about 15%. The large number of very small
classes tends to increase this error rate. In this paper, we do an initial
quick comparison of our classes with literature templates. This is the first
time that an automatic, objective and robust unsupervised classification is
established on such a large number of galaxy spectra. The mean spectra of the
classes can be used as templates for a large majority of galaxies in our
Universe.
