  Aims: We show the use of principal component analysis (PCA) and Fourier
decomposition (FD) method as tools for variable star diagnostics and compare
their relative performance in studying the changes in the light curve
structures of pulsating Cepheids and in the classification of variable stars.
Methods: We have calculated the Fourier parameters of 17,606 light curves of a
variety of variables, e.g., RR Lyraes, Cepheids, Mira Variables and extrinsic
variables for our analysis. We have also performed PCA on the same database of
light curves. The inputs to the PCA are the 100 values of the magnitudes for
each of these 17,606 light curves in the database interpolated between phase 0
to 1. Unlike some previous studies, Fourier coefficients are not used as input
to the PCA. Results: We show that in general, the first few principal
components (PCs) are enough to reconstruct the original light curves compared
to FD method where 2 to 3 times more number of parameters are required to
satisfactorily reconstruct the light curves. The computation of required number
of Fourier parameters on the average needs 20 times more CPU time than the
computation of required number of PCs. Therefore, PCA does have some advantage
over the FD method in analysing the variable stars in a larger database.
However in some cases, particularly in finding the resonances in Fundamental
mode (FU) Cepheids, the PCA results show no distinct advantages over the FD
method. We also demonstrate that the PCA technique can be used to classify
variables into different variability classes in an automated, unsupervised way,
a feature that has immense potential for larger databases of the future.
