Latent feature regression for multivariate count data
Arto Klami, Abhishek Kumar Tripathi, Jalmar Johannes Sirola, Lauri Väre, Frédéric Roulland · International Conference on Artificial Intelligence and Statistics · 2015
We consider the problem of regression on multivariate count data and present a Gibbs sampler for a latent feature regression model suitable for both under- and overdispersed response variables. The model learns countvalued latent features conditional on arbitrary covariates, modeling them as negative binomial variables, and maps them into the dependent count-valued observations using a Dirichlet-multinomial distribution. From another viewpoint, the model can be seen as a generalization of a specic topic model for scenarios where we are interested in generating the actual counts of observations and not just their relative frequencies and cooccurrences. The model is demonstrated on a smart trac