A mixture of multiple linear classifiers with sample weight and manifold regularization

Weite Li, Benhui Chen, Bo Zhou, Jinglu Hu

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

1 Citation (Scopus)

Abstract

A mixture of multiple linear classifiers is famous for its efficiency and effectiveness to tackle nonlinear classification problems. Each classifier contains one linear function multiplied with a gated function, which restricts its corresponding classifier to a local region. Previous researches mainly focus on the partition of local regions, since its quality directly determines the performance of mixture models. However, in real-world data sets, imbalanced and insufficient labeled data are two frequently encountered problems, which also have large influences on the performance of learned classifiers but are seldom considered or explored in the context of mixture models. In this paper, these missing components are introduced into the original formulation of mixture models, namely, a sample weighting scheme for imbalanced data distributions and a manifold regularization to leverage unlabeled data. Then, two solutions with closed form are provided for parameter optimization. Experimental results in the end of our paper exhibit the significance of the added components. As a result, a mixture of multiple linear classifiers can be extended to imbalanced and semi-supervised learning problems.

Original languageEnglish
Title of host publication2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3747-3752
Number of pages6
ISBN (Electronic)9781509061815
DOIs
Publication statusPublished - 2017 Jun 30
Event2017 International Joint Conference on Neural Networks, IJCNN 2017 - Anchorage, United States
Duration: 2017 May 142017 May 19

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2017-May

Other

Other2017 International Joint Conference on Neural Networks, IJCNN 2017
Country/TerritoryUnited States
CityAnchorage
Period17/5/1417/5/19

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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