Higher-Order Markov Tag-Topic Models for Tagged Documents and Images

TL;DR

This paper introduces Higher-Order Markov Tag-Topic Models (TTM) that leverage higher-order relations among tagged documents and images within an MRF framework, improving topic modeling performance in text and image analysis tasks.

Contribution

The paper presents a novel TTM framework with factor graph and hypergraph representations, along with efficient loopy BP algorithms for modeling higher-order relations in tagged data.

Findings

Higher-order relations improve topic modeling accuracy.

TTM outperforms state-of-the-art models in text and image tasks.

Effective in word/link prediction, classification, and tag recommendation.

Abstract

This paper studies the topic modeling problem of tagged documents and images. Higher-order relations among tagged documents and images are major and ubiquitous characteristics, and play positive roles in extracting reliable and interpretable topics. In this paper, we propose the tag-topic models (TTM) to depict such higher-order topic structural dependencies within the Markov random field (MRF) framework. First, we use the novel factor graph representation of latent Dirichlet allocation (LDA)-based topic models from the MRF perspective, and present an efficient loopy belief propagation (BP) algorithm for approximate inference and parameter estimation. Second, we propose the factor hypergraph representation of TTM, and focus on both pairwise and higher-order relation modeling among tagged documents and images. Efficient loopy BP algorithm is developed to learn TTM, which encourages the topic labeling smoothness among tagged documents and images. Extensive experimental results confirm the incorporation of higher-order relations to be effective in enhancing the overall topic modeling performance, when compared with current state-of-the-art topic models, in many text and image mining tasks of broad interests such as word and link prediction, document classification, and tag recommendation.