Structural properties and classification of variable stars: A study
  through unsupervised machine learning techniques
  The advancement in the field of data science especially in machine learning
along with vast databases of variable star projects like the Optical
Gravitational Lensing Experiment (OGLE) encourages researchers to analyse as
well as classify light curves of different variable stars automatically with
efficiency. In the present work, we have demonstrated the relative performances
of principal component analysis (PCA) and independent component analysis (ICA)
applying to huge databases of OGLE variable star light curves after obtaining
1000 magnitudes between phase 0 to 1 with step length 0.001 for each light
curves in identifying resonances for fundamental mode (FU) and first overtone
(FO) Cepheids and in the classification of variable stars for Large Magellanic
Cloud (LMC), Small Magellanic Cloud (SMC) as well as Milky Way (MW). We have
seen that the performance of ICA is better for finding resonances for Cepheid
variables as well as for accurately classifying large data sets of light curves
than PCA. Using K-means clustering algorithm (CA) with respect to independent
components (ICs), we have plotted period-luminosity diagrams and
colour-magnitude diagrams separately for LMC, SMC and MW and found that ICA
along with K-means CA is a very robust tool for classification as well as
future prediction on the nature of light curves of variable stars.
