A quick aside. I was thinking about how response variables are attached to generative models. For instance, if we want to say have binary classification on documents we would normally 1) take the dot product the topic vector with a global vector of coefficients and then 2) pass it through a logistic function. This is fine, but the problem is that if we were sampling the topic model using a Gibbs sampler we have to use some form of gradient descent to compute the dot product coefficients and to compute the coefficients of the logistic function.
This is painful. I’d rather have everything be probabilistic. I was pondering on ways to do this. Let’s say that the topic vector has dimension . Define two multinomial distributions and . Now define the response through this procedure
- Given topic distribution
Essentially, the idea is and end up finding mutually exclusive dimensions such that either one or the other has a high value in order to produce the correct class label with high probability. I’d like to try it after I get done with the Gibbs code.