Semisupervised learning of mixture models with class constraints

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

    1 Scopus citations

    Abstract

    Most prior work on semisupervised clustering/mixture modeling with given class constraints assumes the number of classes is known, with each learned cluster assumed to be a class and, hence, subject to the given instance-level constraints. When the number of classes is incorrectly assumed and/or when the "one-cluster-perclass" assumption is not valid, the use of constraint information in these methods may actually be deleterious to learning the ground-truth data groups. In this work we extend semisupervised learning with constraints 1) to allow allocation of multiple mixture components to individual classes and 2) to estimate both the number of components/clusters and, leveraging the constraint information, the number of classes present in the data. For several real-world data sets, our method is shown to correctly estimate the number of classes and to give a favorable comparison with the recent mixture modeling approach of Shental et al.

    Original languageEnglish (US)
    Title of host publication2005 IEEE ICASSP '05 - Proc. - Design and Implementation of Signal Proces.Syst.,Indust. Technol. Track,Machine Learning for Signal Proces. Education, Spec. Sessions
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    PagesV185-V188
    ISBN (Print)0780388747, 9780780388741
    DOIs
    StatePublished - Jan 1 2005
    Event2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05 - Philadelphia, PA, United States
    Duration: Mar 18 2005Mar 23 2005

    Publication series

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

    Other

    Other2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05
    CountryUnited States
    CityPhiladelphia, PA
    Period3/18/053/23/05

    All Science Journal Classification (ASJC) codes

    • Software
    • Signal Processing
    • Electrical and Electronic Engineering

    Cite this

    Zhao, Q., & Miller, D. J. (2005). Semisupervised learning of mixture models with class constraints. In 2005 IEEE ICASSP '05 - Proc. - Design and Implementation of Signal Proces.Syst.,Indust. Technol. Track,Machine Learning for Signal Proces. Education, Spec. Sessions (pp. V185-V188). [1416271] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. V). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2005.1416271