@inproceedings{fb508b4c8a6446208d0a62d09d88e1c5,
title = "Regularizing flat latent variables with hierarchical structures",
abstract = "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.",
author = "Rongcheng Lin and Huayu Li and Xiaojun Quan and Richang Hong and Zhiang Wu and Yong Ge",
year = "2015",
language = "English (US)",
series = "IJCAI International Joint Conference on Artificial Intelligence",
publisher = "International Joint Conferences on Artificial Intelligence",
pages = "3671--3677",
editor = "Michael Wooldridge and Qiang Yang",
booktitle = "IJCAI 2015 - Proceedings of the 24th International Joint Conference on Artificial Intelligence",
note = "24th International Joint Conference on Artificial Intelligence, IJCAI 2015 ; Conference date: 25-07-2015 Through 31-07-2015",
}