  We present new findings in regard to data analysis in very high dimensional
spaces. We use dimensionalities up to around one million. A particular benefit
of Correspondence Analysis is its suitability for carrying out an orthonormal
mapping, or scaling, of power law distributed data. Power law distributed data
are found in many domains. Correspondence factor analysis provides a latent
semantic or principal axes mapping. Our experiments use data from digital
chemistry and finance, and other statistically generated data.
