  Incorporating the side information of text corpus, i.e., authors, time
stamps, and emotional tags, into the traditional text mining models has gained
significant interests in the area of information retrieval, statistical natural
language processing, and machine learning. One branch of these works is the
so-called Author Topic Model (ATM), which incorporates the authors's interests
as side information into the classical topic model. However, the existing ATM
needs to predefine the number of topics, which is difficult and inappropriate
in many real-world settings. In this paper, we propose an Infinite Author Topic
(IAT) model to resolve this issue. Instead of assigning a discrete probability
on fixed number of topics, we use a stochastic process to determine the number
of topics from the data itself. To be specific, we extend a gamma-negative
binomial process to three levels in order to capture the
author-document-keyword hierarchical structure. Furthermore, each document is
assigned a mixed gamma process that accounts for the multi-author's
contribution towards this document. An efficient Gibbs sampling inference
algorithm with each conditional distribution being closed-form is developed for
the IAT model. Experiments on several real-world datasets show the capabilities
of our IAT model to learn the hidden topics, authors' interests on these topics
and the number of topics simultaneously.

Machine Learning;Information Retrieval;Machine Learning