  Linear discriminant analysis (LDA) is a popular tool for classification and
dimension reduction. Limited by its linear form and the underlying Gaussian
assumption, however, LDA is not applicable in situations where the data
distribution is complex. Recently, we proposed a discriminative normalization
flow (DNF) model. In this study, we reinterpret DNF as a deep generative LDA
model, and study its properties in representing complex data. We conducted a
simulation experiment and a speaker recognition experiment. The results show
that DNF and its subspace version are much more powerful than the conventional
LDA in modeling complex data and retrieving low-dimensional representations.
