Class dependent cluster refinement

Jakob Sternby

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceedingResearchpeer-review

Abstract

Unsupervised classification is a very common problem in pattern recognition even when the classes are known. In many areas intra-class variations may be greater than the inter-class variations causing a need for a subdivision of the training set of a class into smaller subunits often referred to as clusters. The subdivision or clustering is often performed independently of the relative properties of the other present classes in the recognition task. This paper presents a novel class-dependent approach to the clustering problem. Experiments with online handwriting data show that the novel clustering approach CDCR produces a clustering better suited for the task of pattern recognition. Although only validated for two recognition methods in this paper, the same approach could be applied to other methods as well as to other pattern recognition problems.
Original languageEnglish
Title of host publicationProceedings - International Conference on Pattern Recognition
PublisherIEEE - Institute of Electrical and Electronics Engineers Inc.
Pages833-836
Volume2
DOIs
Publication statusPublished - 2006
Event18th International Conference on Pattern Recognition, ICPR 2006 - Hong Kong, China
Duration: 2006 Aug 202006 Aug 24

Publication series

Name
Volume2
ISSN (Print)1051-4651

Conference

Conference18th International Conference on Pattern Recognition, ICPR 2006
Country/TerritoryChina
CityHong Kong
Period2006/08/202006/08/24

Subject classification (UKÄ)

  • Mathematics

Keywords

  • Unsupervised classification
  • Clustering problems

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