Factorized Topic Models
Abstract
In this paper we present a modification to a latent topic model, which makes the model exploit supervision to produce a factorized representation of the observed data. The structured parameterization separately encodes variance that is shared between classes from variance that is private to each class by the introduction of a new prior over the topic space. The approach allows for a more eff{}icient inference and provides an intuitive interpretation of the data in terms of an informative signal together with structured noise. The factorized representation is shown to enhance inference performance for image, text, and video classification.
Cite
Text
Zhang et al. "Factorized Topic Models." International Conference on Learning Representations, 2013.Markdown
[Zhang et al. "Factorized Topic Models." International Conference on Learning Representations, 2013.](https://mlanthology.org/iclr/2013/zhang2013iclr-factorized/)BibTeX
@inproceedings{zhang2013iclr-factorized,
title = {{Factorized Topic Models}},
author = {Zhang, Cheng and Ek, Carl Henrik and Kjellström, Hedvig},
booktitle = {International Conference on Learning Representations},
year = {2013},
url = {https://mlanthology.org/iclr/2013/zhang2013iclr-factorized/}
}