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 - 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
Country/TerritoryUnited States
CityPhiladelphia, PA
Period3/18/053/23/05

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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