  Correspondence analysis is a dimension reduction method for visualization of
nonnegative data sets, in particular contingency tables ; but it depends on the
marginals of the data set. Two transformations of the data have been proposed
to render correspondence analysis row and column scales invariant : These two
kinds of transformations change the initial form of the data set into a
bistochastic form. The power transorfmation applied by Greenacre (2010) has one
positive parameter. While the transormation applied by Mosteller (1968) and
Goodman (1996) has (I+J) positive parameters, where the raw data is row and
column scaled by the Sinkhorn (RAS or ipf) algorithm to render it bistochastic.
Goodman (1996) named correspondence analsis of a bistochastic matrix
marginal-free correspondence analysis. We discuss these two transformations,
and further generalize Mosteller-Goodman approach.
