Finding hidden events in astrophysical data using PCA and Mixture of Gaussians clustering
The Principal Component Analysis (PCA) is applied to a set of astronomic data to obtain a separation between variations of luminosity and noisy fluctuations. A clustering with the Mixture of Gaussians method, performed in the principal subspace, allows us to classify the data according to the features of interest. Our results are compared with those obtained by the AGAPE (Andromeda Galaxy and Amplified Pixels Experiment) collaboration.
astrophysical data mixture of gaussians principal component analysis classification data analysis pixel lensing