  Principal Component Analysis (PCA) is being extensively used in Astronomy but
not yet exhaustively exploited for variability search. The aim of this work is
to investigate the effectiveness of using the PCA as a method to search for
variable stars in large photometric data sets. We apply PCA to variability
indices computed for light curves of 18152 stars in three fields in M 31
extracted from the Hubble Source Catalogue. The projection of the data into the
principal components is used as a stellar variability detection and
classification tool, capable of distinguishing between RR Lyrae stars, long
period variables (LPVs) and non-variables. This projection recovered more than
90% of the known variables and revealed 38 previously unknown variable stars
(about 30% more), all LPVs except for one object of uncertain variability type.
We conclude that this methodology can indeed successfully identify candidate
variable stars.
