ML Anthology

Regularizing Flat Latent Variables with Hierarchical Structures

Rongcheng Lin, Huayu Li, Xiaojun Quan, Richang Hong, Zhiang Wu, Yong Ge
IJCAI 2015 pp. 3671-3677
/ijcai/2015/lin2015ijcai-regularizing/

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.

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Cite

Text

Lin et al. "Regularizing Flat Latent Variables with Hierarchical Structures." International Joint Conference on Artificial Intelligence, 2015.

Markdown

[Lin et al. "Regularizing Flat Latent Variables with Hierarchical Structures." International Joint Conference on Artificial Intelligence, 2015.](https://mlanthology.org/ijcai/2015/lin2015ijcai-regularizing/)

BibTeX

@inproceedings{lin2015ijcai-regularizing,
  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 = {International Joint Conference on Artificial Intelligence},
  year      = {2015},
  pages     = {3671-3677},
  url       = {https://mlanthology.org/ijcai/2015/lin2015ijcai-regularizing/}
}