Regularizing Flat Latent Variables with Hierarchical Structures
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Special Track on Machine Learning. Pages 3671-3676.
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.
@inproceedings{ijcai2015p516,
title = {Regularizing Flat Latent Variables with Hierarchical Structures},
author = {Lin, Rongcheng and Li, Huayu and Quan, Xiaojun and Hong, Richang and Wu, Zhiang and Ge, Yong},
booktitle = {Proceedings of the Twenty-Fourth International Joint Conference on
Artificial Intelligence, {IJCAI-15}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Qiang Yang and Michael Wooldridge},
pages = {3671--3676},
year = {2015},
month = {7},
note = {Special Track on Machine Learning},
url = {https://ijcai.org/proceedings/2015/516},
}
TY - CPAPER TI - Regularizing Flat Latent Variables with Hierarchical Structures AU - Lin, Rongcheng AU - Li, Huayu AU - Quan, Xiaojun AU - Hong, Richang AU - Wu, Zhiang AU - Ge, Yong T2 - Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15 PB - International Joint Conferences on Artificial Intelligence PY - 2015 SP - 3671 EP - 3676 UR - https://ijcai.org/proceedings/2015/516 ER -Copied
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