Introduction of a new class TPrincipal implemented by Christian Holm Cristensen.

The Principal Components Analysis class
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In many applications of various fields of research, the treatment of
large amounts of data requires powerful techniques capable of rapid
data reduction and analysis. Usually, the quantities most
conveniently measured by the experimentalist, are not necessarily the
most significant for classification and analysis of the data. It is
then useful to have a way of selecting an optimal set of variables
necessary for the recognition process and reducing the dimensionality
of the problem, resulting in an easier classification procedure.

TPrincipal is the implementation of one such method of
feature selection, namely the principal components analysis.
This multidimensional technique is well known in the field of pattern
recognition and and its use in Particle Physics has been documented
elsewhere (cf. H. Wind, <I>Function Parameterization</I>, CERN
72-21).


git-svn-id: http://root.cern.ch/svn/root/trunk@459 27541ba8-7e3a-0410-8455-c3a389f83636