Regularizing Flat Latent Variables with Hierarchical Structures

In this paper, we propose a stratified topic model (STM). Instead of directly modeling and inferring flat topics or hierarchically structured topics, we use the stratified relationships in topic hierarchies to regularize the flat topics. The topic structures are captured by a hierarchical clustering method and play as constraints during the learning process. We propose two theoretically sound and practical inference methods to solve the model. Experimental results with two real world data sets and various evaluation metrics demonstrate the effectiveness of the proposed model.

Authors

• Rongcheng Lin
• Huayu Li The University of Arizona
• Xiaojun Quan School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
• Richang Hong Hefei University of Technology
• Zhiang Wu
• Yong Ge

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