How to supervise topic models

Abstract

Supervised topic models are important machine learning tools which have been widely used in computer vision as well as in other domains. However, there is a gap in the understanding of the supervision impact on the model. In this paper, we present a thorough analysis on the behaviour of supervised topic models using Supervised Latent Dirichlet Allocation (SLDA) and propose two factorized supervised topic models, which factorize the topics into signal and noise. Experimental results on both synthetic data and real-world data for computer vision tasks show that supervision need to be boosted to be effective and factorized topic models are able to enhance the performance.