  Distortion is a fundamental well-studied topic in dimension reduction papers,
and intimately related with the underlying intrinsic dimension of a mapping of
a high dimensional data set onto a lower dimension. In this paper, we study
embedding distortions produced by Correspondence Analysis and its robust l1
variant Taxicab Correspondence analysis, which are visualization methods for
contingency tables. For high dimensional data, distortions in Correspondence
Analysis are contractions; while distortions in Taxicab Correspondence Analysis
could be contractions or stretchings. This shows that Euclidean geometry is
quite rigid, because of the orthogonality property; while Taxicab geometry is
quite flexible, because the orthogonality property is replaced by the conjugacy
property.
