Supervised Hierarchical Dirichlet Processes with Variational Inference
2013 (English)In: 2013 IEEE International Conference on Computer Vision Workshops (ICCVW), IEEE , 2013, 254-261 p.Conference paper (Refereed)
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.
Place, publisher, year, edition, pages
IEEE , 2013. 254-261 p.
Topic Modeling, HDP, Supervised HDP, Dirichlet Processes
Computer Vision and Robotics (Autonomous Systems)
IdentifiersURN: urn:nbn:se:kth:diva-134128DOI: 10.1109/ICCVW.2013.41ISI: 000349847200036ScopusID: 2-s2.0-84897533026ISBN: 978-147993022-7OAI: oai:DiVA.org:kth-134128DiVA: diva2:664983
2013 14th IEEE International Conference on Computer Vision Workshops, ICCVW 2013; Sydney, NSW; Australia; 1 December 2013 through 8 December 2013
QC 201312172013-11-182013-11-182015-10-06Bibliographically approved