Likelihood estimation of sparse topic distributions in topic models and its applications to Wasserstein document distance calculations
主题模型中稀疏主题分布的似然估计及 Wasserstein 文档距离应用
This paper studies the estimation of high-dimensional, discrete, possibly sparse, mixture models in the context of topic models. The data consists of observed multinomial counts of p words across n independent documents. In topic models, the p×n expected word frequency matrix is assumed to be factorized as a p×K word-topic matrix A and a K×n topic-document matrix T. Since columns of both matrices represent conditional probabilities belonging to probability simplices, columns…