We develop new models and algorithms for learning the temporal dynamics of the topic polytopes and related geometric objects that arise in topic model based inference. Our model is nonparametric Bayesian and the corresponding inference algorithm is able to discover new topics as the time progresses. By exploiting the connection between the modeling of topic polytope evolution, Beta-Bernoulli process and the Hungarian matching algorithm, our method is shown to be several orders of magnitude faster than existing topic modeling approaches, as demonstrated by experiments working with several million documents in under two dozens of minutes.
翻译:我们开发了新的模型和算法,用于学习专题多面形及相关几何天体的时间动态,这些是在基于主题的模型推理中产生的。我们的模型是非参数贝叶斯式的,相应的推理算法能够随着时间的进展发现新的专题。通过利用多面体演变、Beta-Bernoulli进程和匈牙利匹配算法等专题模型的建模之间的联系,我们的方法比现有的专题建模方法要快几级,正如在两十分钟内对数百万份文件进行的实验所证明的。