On kernel fuzzy c-means for data with tolerance using explicit mapping for kernel data analysis

Yuchi Kanzawa, Yasunori Endo, Sadaaki Miyamoto

Research output: Contribution to journalArticle

Abstract

While explicit mapping is generally unknown for kernel data analysis, its inner product should be known. Although we proposed a kernel fuzzy c-means algorithm for data with tolerance, cluster centers and tolerance in higher dimensional space have not been seen. Contrary to this common assumption, explicit mapping has been introduced and the situation of kernel fuzzy c-means in higher dimensional space has been described via kernel principal component analysis using explicit mapping. In this paper, cluster centers and the tolerance of kernel fuzzy c-means for data with olerance are described via kernel principal component analysis using explicit mapping.

Original languageEnglish
Pages (from-to)162-168
Number of pages7
JournalJournal of Advanced Computational Intelligence and Intelligent Informatics
Volume16
Issue number1
DOIs
Publication statusPublished - 2012 Jan

Keywords

  • Explicit mapping
  • Fuzzy c-means
  • Kernel data analysis
  • Tolerance

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'On kernel fuzzy c-means for data with tolerance using explicit mapping for kernel data analysis'. Together they form a unique fingerprint.

  • Cite this