Abstract

We introduce the Spherical Admixture Model (SAM), a Bayesian topic model for arbitrary normalized data. SAM maintains the same hierarchical structure as Latent Dirichlet Allocation (LDA), but models documents as points on a high-dimensional spherical manifold, allowing a natural likelihood parameterization in terms of cosine distance. Furthermore, SAM can model word absence/presence at the document level, and unlike previous models can assign explicit negative weight to topic terms. Performance is evaluated empirically, both through human ratings of topic quality and through diverse classification tasks from natural language processing and computer vision. In these experiments, SAM consistently outperforms existing models.