Reconstructing equation of state of dark energy with principal component
  analysis
  We represent a nonparametric method to reconstruct the equation of state for
dark energy directly from observational Hubble parameter data. We use principal
component analysis (PCA) to extract the signal from data with noise. Moreover,
we modify Akaike information criterion (AIC) to guarantee the quality of
reconstruction and avoid over-fitting simultaneously. The results show that our
method is robust in reconstruction of dark energy equation of state. Although
current observational Hubble parameter data alone can not give a strong
constraint yet, our results indicate that future observations can significantly
improve the quality of the reconstruction.
