  We propose a robust principal component analysis (PCA) framework for the
exploitation of multi-band photometric measurements in large surveys. Period
search results are improved using the time series of the first principal
component due to its optimized signal-to-noise ratio.The presence of correlated
excess variations in the multivariate time series enables the detection of
weaker variability. Furthermore, the direction of the largest variance differs
for certain types of variable stars. This can be used as an efficient attribute
for classification. The application of the method to a subsample of Sloan
Digital Sky Survey Stripe 82 data yielded 132 high-amplitude Delta Scuti
variables. We found also 129 new RR Lyrae variables, complementary to the
catalogue of Sesar et al., 2010, extending the halo area mapped by Stripe 82 RR
Lyrae stars towards the Galactic bulge. The sample comprises also 25
multiperiodic or Blazhko RR Lyrae stars.
