The application of principal components analysis to astrophysics

Details The application of principal components analysis to astrophysics The application of principal components analysis to astrophysics

Mar 2001

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2001pabei..19....9l&link_type=abstract

Progress in Astronomy, Vol. 19, No. 1, p. 9 - 16 (2001)

Astronomy and Astrophysics

Astronomy

2

Principal Component Analysis, Variance, Covariance Matrix, Galaxies

Scientific paper

Principal component analysis (PCA) is a main multivariate statistical method for getting principal information from observational data. It uses few new variables instead of initial parameters, in order to find out the relations among the initial parameters, without losing the main information of initial data. Especially for the case of large sample and multivariate, this method is simpler and more efficient. In the present day, PCA is applied widely in many research fields of astrophysics. The main principle and applications to astrophysics of PCA are reviewed in this paper.

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