Florian T. Pokorny
Assistant Professor, School of Computer Science and Communication, KTH Royal Institute of TechnologySupervised Hierarchical Dirichlet Processes with Variational Inference
In IEEE ICCV Workshop on Inference for Probabilistic Graphical Models, 2013
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.
Files
Download this publicationBibtex
@article{zhang2013a,
title = {Supervised Hierarchical Dirichlet Processes with Variational Inference},
author = {Zhang, Cheng and Ek, Carl Henrik and Gratal, Xavi and Pokorny, Florian T. and Kjellstr{\"o}m, Hedvig},
journal = {IEEE ICCV Workshop on Inference for Probabilistic Graphical Models},
year = {2013},
url = {http://www.csc.kth.se/~fpokorny/static/publications/zhang2013a.pdf},
}