|You are working with the text-only light edition of "H.Lohninger: Teach/Me Data Analysis, Springer-Verlag, Berlin-New York-Tokyo, 1999. ISBN 3-540-14743-8". Click here for further information.|
|See also: PCA, eigenvectors|
Principal component analysis can be considered from the viewpoint of
data compression. A few scores of the PCA and the corresponding loading
vectors can be used to estimate the contents of a large data matrix. The
idea behind this is that by reducing the number of eigenvectors used to
reconstruct the original data matrix, the amount of required storage space
is reduced. However one should be careful, since this method of compression
is only meaningful if the data matrix shows a high amount of correlation,
both among the variables and the objects.
Click on the logo above to start an interactive example which shows some details of this method.
Last Update: 2004-Jul-03