On Discriminative Joint Density Modeling
Jarkko Salojärvi, Kai Puolamäki and Samuel Kaski
In: ECML 2005, 3-7 Oct 2005, Porto, Portugal.
We study discriminative joint density models, that is, generative
models for the joint density p(c,x) learned by maximizing a
discriminative cost function, the conditional likelihood. We use the
framework to derive generative models for generalized linear models,
including logistic regression, linear discriminant analysis, and
discriminative mixture of unigrams. The benefits of deriving
the discriminative models from joint density models are that it is
easy to extend the models and interpret the results, and missing
data can be treated using justified standard methods.