Principal Component Analysis of SDSS Stellar Spectra
  We apply Principal Component Analysis (PCA) to ~100,000 stellar spectra
obtained by the Sloan Digital Sky Survey (SDSS). In order to avoid strong
non-linear variation of spectra with effective temperature, the sample is
binned into 0.02 mag wide intervals of the g-r color (-0.20<g-r<0.90, roughly
corresponding to MK spectral types A3 to K3), and PCA is applied independently
for each bin. In each color bin, the first four eigenspectra are sufficient to
describe the observed spectra within the measurement noise. We discuss
correlations of eigencoefficients with metallicity and gravity estimated by the
Sloan Extension for Galactic Understanding and Exploration (SEGUE) Stellar
Parameters Pipeline. The resulting high signal-to-noise mean spectra and the
other three eigenspectra are made publicly available. These data can be used to
generate high quality spectra for an arbitrary combination of effective
temperature, metallicity, and gravity within the parameter space probed by the
SDSS. The SDSS stellar spectroscopic database and the PCA results presented
here offer a convenient method to classify new spectra, to search for unusual
spectra, to train various spectral classification methods, and to synthesize
accurate colors in arbitrary optical bandpasses.
