Reconstruction of late-time cosmology using Principal Component Analysis
  We reconstruct late-time cosmology using the technique of Principal Component
Analysis (PCA). In particular, we focus on the reconstruction of the dark
energy equation of state from two different observational data-sets, Supernovae
type Ia data, and Hubble parameter data. The analysis is carried out in two
different approaches. The first one is a derived approach, where we reconstruct
the observable quantity using PCA and subsequently construct the equation of
state parameter. The other approach is the direct reconstruction of the
equation of state from the data. A combination of PCA algorithm and calculation
of correlation coefficients are used as prime tools of reconstruction. We carry
out the analysis with simulated data as well as with real data. The derived
approach is found to be statistically preferable over the direct approach. The
reconstructed equation of state indicates a slowly varying equation of state of
dark energy.
