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Open Access Publications from the University of California

Majorizing Logit Loss Functions for the Multidimensional Representation of Categorical Data

  • Author(s): de Leeuw, Jan
  • et al.

The data we study are measurements of n objects on each of m variables. This corresponds with the spreadsheet format in many software packages and with the data-frame in S/R. In our setup all data are categorical, which means each variable maps the observations into a finite number of categories. Categories can be a finite subset of the reals, a finite ordered set, the set {0,1}, or just an arbitrary finite set.

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