Learning Local Semantic Distances with Limited Supervision

Shiyu Chang, Charu C. Aggarwal, Thomas S. Huang

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

Abstract

Recent advances in distance function learning have demonstrated that learning a good distance metric can greatly improve the performance in a wide variety of tasks in data mining and web search. A major problem in such scenarios is the limited labeled knowledge available for learning the user intentions. Furthermore, distances are inherently local, where a single global distance function may not capture the distance structure well. A challenge here is that local distance learning is even harder when the labeled information available is limited, because the distance function varies with data locality. To address these issues, we propose a local metric learning algorithm termed Local Semantic Sensing (LSS), which augments the small amount of labeled data with unlabeled data in order to learn the semantic information in the manifold structure, and then integrated with supervised intentional knowledge in a local way. We present results in a retrieval application, which show that the approach significantly outperforms other state-of-the-art methods in the literature.

Original languageEnglish (US)
Title of host publicationProceedings - 14th IEEE International Conference on Data Mining, ICDM 2014
EditorsRavi Kumar, Hannu Toivonen, Jian Pei, Joshua Zhexue Huang, Xindong Wu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages70-79
Number of pages10
EditionJanuary
ISBN (Electronic)9781479943029
DOIs
StatePublished - Jan 1 2014
Event14th IEEE International Conference on Data Mining, ICDM 2014 - Shenzhen, China
Duration: Dec 14 2014Dec 17 2014

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
NumberJanuary
Volume2015-January
ISSN (Print)1550-4786

Other

Other14th IEEE International Conference on Data Mining, ICDM 2014
CountryChina
CityShenzhen
Period12/14/1412/17/14

Keywords

  • Instance based
  • Metric learning
  • Semantic Aware
  • Semi-supervised
  • Similarity learning

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Chang, S., Aggarwal, C. C., & Huang, T. S. (2014). Learning Local Semantic Distances with Limited Supervision. In R. Kumar, H. Toivonen, J. Pei, J. Zhexue Huang, & X. Wu (Eds.), Proceedings - 14th IEEE International Conference on Data Mining, ICDM 2014 (January ed., pp. 70-79). [7023324] (Proceedings - IEEE International Conference on Data Mining, ICDM; Vol. 2015-January, No. January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDM.2014.114