Probabilistic topic models have become popular as methods
for dimensionality reduction in collections of text documents
or images.  These models are usually treated as generative
models and trained using maximum likelihood or Bayesian methods.
In this paper, we discuss an alternative: a discriminative framework
in which we assume that supervised side information is present,
and in which we wish to take that side information into account
in finding  a reduced dimensionality representation.  Specifically,
we present DiscLDA, a discriminative variation on Latent
Dirichlet Allocation (LDA) in which a class-dependent linear
transformation is introduced on the topic mixture proportions.
This parameter is estimated by maximizing the conditional
likelihood.  By using the transformed topic mixture proportions
as a new representation of documents, we obtain a supervised
dimensionality reduction algorithm that uncovers the latent
structure in a document collection while preserving predictive
power for the task of classification. We compare the predictive
power of the latent structure of DiscLDA with unsupervised LDA
on the 20 Newsgroups document classification task and show how
our model can identify shared topics across classes as well as
class-dependent topics.