A new topic-bridged model for transfer learning

Meng Sung Wu*, Jen-Tzung Chien

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

In real-world information systems, there are abundant unlabeled data but sparse labeled data. It is challenging to construct an adaptive model to classify a large amount of documents containing different domains. The classifiers trained from a source domain shall perform poorly for the test data in a target domain due to the domain mismatch. In this study, we build a topic-bridged latent Dirichlet allocation (TLDA) model from a variety of labeled and unlabeled documents and perform the transfer learning for document classification. The severe change of word distributions is compensated by bridging the latent topics of source and target data which are drawn by the Dirichlet priors. A variational inference procedure is performed for semi-supervised learning. In the experiments on text categorization using 20 Newsgroups dataset, the proposed TLDA model achieved higher classification performance compared to the other methods.

Original languageEnglish
Title of host publication2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings
Pages5346-5349
Number of pages4
DOIs
StatePublished - 8 Nov 2010
Event2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Dallas, TX, United States
Duration: 14 Mar 201019 Mar 2010

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010
CountryUnited States
CityDallas, TX
Period14/03/1019/03/10

Keywords

  • Bayes procedures
  • Pattern classification
  • Text processing
  • Text recognition

Fingerprint Dive into the research topics of 'A new topic-bridged model for transfer learning'. Together they form a unique fingerprint.

  • Cite this

    Wu, M. S., & Chien, J-T. (2010). A new topic-bridged model for transfer learning. In 2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings (pp. 5346-5349). [5494947] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2010.5494947