Topics over Nonparametric Time: A Supervised Topic Model Using Bayesian Nonparametric Density Estimation
Abstract
We propose a new supervised topic model that uses a nonparametric density estimator to model the distribution of real-valued metadata given a topic. The model is similar to Topics Over Time, but replaces the beta distributions used in that model with a Dirichlet process mixture of normals. The use of a nonparametric density estimator allows for the fitting of a greater class of metadata densities. We compare our model with existing supervised topic models in terms of prediction and show that it is capable of discovering complex metadata distributions in both synthetic and real data. 1
Cite
Text
Walker et al. "Topics over Nonparametric Time: A Supervised Topic Model Using Bayesian Nonparametric Density Estimation." Conference on Uncertainty in Artificial Intelligence, 2012.Markdown
[Walker et al. "Topics over Nonparametric Time: A Supervised Topic Model Using Bayesian Nonparametric Density Estimation." Conference on Uncertainty in Artificial Intelligence, 2012.](https://mlanthology.org/uai/2012/walker2012uai-topics/)BibTeX
@inproceedings{walker2012uai-topics,
title = {{Topics over Nonparametric Time: A Supervised Topic Model Using Bayesian Nonparametric Density Estimation}},
author = {Walker, Daniel David and Ringger, Eric K. and Seppi, Kevin D.},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2012},
pages = {74-83},
url = {https://mlanthology.org/uai/2012/walker2012uai-topics/}
}