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Incorporating the side information of text corpus, i.e., authors, timestamps, and emotional tags, into the traditional text mining models has gainedsignificant interests in the area of information retrieval, statistical naturallanguage processing, and machine learning. One branch of these works is theso-called Author Topic Model (ATM), which incorporates the authors's interestsas side information into the classical topic model. However, the existing ATMneeds to predefine the number of topics, which is difficult and inappropriatein many real-world settings. In this paper, we propose an Infinite Author Topic(IAT) model to resolve this issue. Instead of assigning a discrete probabilityon fixed number of topics, we use a stochastic process to determine the numberof topics from the data itself. To be specific, we extend a gamma-negativebinomial process to three levels in order to capture theauthor-document-keyword hierarchical structure. Furthermore, each document isassigned a mixed gamma process that accounts for the multi-author'scontribution towards this document. An efficient Gibbs sampling inferencealgorithm with each conditional distribution being closed-form is developed forthe IAT model. Experiments on several real-world datasets show the capabilitiesof our IAT model to learn the hidden topics, authors' interests on these topicsand the number of topics simultaneously.

Infinite Author Topic Model based on Mixed Gamma-Negative Binomial Process