一种基于密度的自适应最优LDA模型选择方法

主题模型(topic models)被广泛应用在信息分类和检索领域.这些模型通过参数估计从文本集合中提取一个低维的多项式分布集合,用于捕获词之间的相关信息,称为主题(topic).针对模型参数学习过程对主题数目的指定和主题分布初始值非常敏感的问题,作者用图的形式阐述了LDA(Latent Dirichlet Allocation)模型中主题产生的过程,提出并证明当主题之间的相似度最小时模型最优的理论;基于该理论,提出了一种基于密度的自适应最优LDA模型选择方法.实验证明该方法可以在不需要人工调试主题数目的情况下,用相对少的迭代,自动找到最优的主题结构.

A Method of Adaptively Selecting Best LDA Model Based on Density

Topic models have been successfully used to information classification and retrieval.These models can capture word correlations in a collection of textual documents with a low-dimensional set of multinomial distribution,called "topics".It is important but difficult to select an appropriate number of topics for a specific dataset.This paper proposes a theorem that the model reaches optimum as the average similarity among topics reaches minimum,and based on this theorem,proposes a method of adaptively selecting the best LDA model based on density.Experiments show that the proposed method can achieve performance matching the best of LDA without manually tuning the number of topics.