Confidence-aware graph regularization with heterogeneous pairwise features

Yuan Fang, Bo June Hsu, Kevin Chen Chuan Chang

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

Abstract

Conventional classification methods tend to focus on features of individual objects, while missing out on potentially valuable pairwise features that capture the relationships between objects. Although recent developments on graph regularization exploit this aspect, existing works generally assume only a single kind of pairwise feature, which is often insufficient. We observe that multiple, heterogeneous pairwise features can often complement each other and are generally more robust in modeling the relationships between objects. Furthermore, as some objects are easier to classify than others, objects with higher initial classification confidence should be weighed more towards classifying related but more ambiguous objects, an observation missing from previous graph regularization techniques. In this paper, we propose a Dirichlet-based regularization framework that supports the combination of heterogeneous pairwise features with confidence-aware prediction using limited labeled training data. Next, we showcase a few applications of our framework in information retrieval, focusing on the problem of query intent classification. Finally, we demonstrate through a series of experiments the advantages of our framework on a large-scale real-world dataset.

Original languageEnglish (US)
Title of host publicationSIGIR'12 - Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval
Pages951-960
Number of pages10
DOIs
StatePublished - Sep 28 2012
Event35th Annual ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2012 - Portland, OR, United States
Duration: Aug 12 2012Aug 16 2012

Publication series

NameSIGIR'12 - Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval

Other

Other35th Annual ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2012
CountryUnited States
CityPortland, OR
Period8/12/128/16/12

Keywords

  • applications in information retrieval
  • confidence
  • graph regularization
  • pairwise features
  • query intent classification

ASJC Scopus subject areas

  • Information Systems

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  • Cite this

    Fang, Y., Hsu, B. J., & Chang, K. C. C. (2012). Confidence-aware graph regularization with heterogeneous pairwise features. In SIGIR'12 - Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 951-960). (SIGIR'12 - Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval). https://doi.org/10.1145/2348283.2348410