Topic models have proven to be a useful tool for discovering latent
structures in document collections. However, most document collections often
come as temporal streams and thus several aspects of the latent structure such
as the number of topics, the topics' distribution and popularity are
time-evolving. Several models exist that model the evolution of some but not
all of the above aspects. In this paper we introduce infinite dynamic topic
models, iDTM, that can accommodate the evolution of all the aforementioned
aspects. Our model assumes that documents are organized into epochs, where the
documents within each epoch are exchangeable but the order between the
documents is maintained across epochs. iDTM allows for unbounded number of
topics: topics can die or be born at any epoch, and the representation of each
topic can evolve according to a Markovian dynamics. We use iDTM to analyze the
birth and evolution of topics in the NIPS community and evaluated the efficacy
of our model on both simulated and real datasets with favorable outcome.

本文提出了无限动态主题模型 (iDTM)，该模型能够适应文档集合中主题数量、主题分布和流行度等潜在结构的时空演化，使用该模型对 NIPS 社区中的主题进行了分析并在模拟和真实数据集上进行了评估，结果良好。