  Despite many years of research into latent Dirichlet allocation (LDA),
applying LDA to collections of non-categorical items is still challenging. Yet
many problems with much richer data share a similar structure and could benefit
from the vast literature on LDA. We propose logistic LDA, a novel
discriminative variant of latent Dirichlet allocation which is easy to apply to
arbitrary inputs. In particular, our model can easily be applied to groups of
images, arbitrary text embeddings, and integrates well with deep neural
networks. Although it is a discriminative model, we show that logistic LDA can
learn from unlabeled data in an unsupervised manner by exploiting the group
structure present in the data. In contrast to other recent topic models
designed to handle arbitrary inputs, our model does not sacrifice the
interpretability and principled motivation of LDA.
