ML Anthology

Supervised Hierarchical Dirichlet Processes with Variational Inference

Cheng Zhang, Carl Henrik Ek, Xavi Gratal, Florian T. Pokorny, Hedvig Kjellström
ICCVW 2013 pp. 254-261
doi:10.1109/ICCVW.2013.41 /iccvw/2013/zhang2013iccvw-supervised/

Abstract

We present an extension to the Hierarchical Dirichlet Process (HDP), which allows for the inclusion of supervision. Our model marries the non-parametric benefits of HDP with those of Supervised Latent Dirichlet Allocation (SLDA) to enable learning the topic space directly from data while simultaneously including the labels within the model. The proposed model is learned using variational inference which allows for the efficient use of a large training dataset. We also present the online version of variational inference, which makes the method scalable to very large datasets. We show results comparing our model to a traditional supervised parametric topic model, SLDA, and show that it outperforms SLDA on a number of benchmark datasets.

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Text

Zhang et al. "Supervised Hierarchical Dirichlet Processes with Variational Inference." IEEE/CVF International Conference on Computer Vision Workshops, 2013. doi:10.1109/ICCVW.2013.41

Markdown

[Zhang et al. "Supervised Hierarchical Dirichlet Processes with Variational Inference." IEEE/CVF International Conference on Computer Vision Workshops, 2013.](https://mlanthology.org/iccvw/2013/zhang2013iccvw-supervised/) doi:10.1109/ICCVW.2013.41

BibTeX

@inproceedings{zhang2013iccvw-supervised,
  title     = {{Supervised Hierarchical Dirichlet Processes with Variational Inference}},
  author    = {Zhang, Cheng and Ek, Carl Henrik and Gratal, Xavi and Pokorny, Florian T. and Kjellström, Hedvig},
  booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
  year      = {2013},
  pages     = {254-261},
  doi       = {10.1109/ICCVW.2013.41},
  url       = {https://mlanthology.org/iccvw/2013/zhang2013iccvw-supervised/}
}