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
Publication statusPublished - 2012 Jan

Fingerprint

Principal component analysis

Keywords

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

ASJC Scopus subject areas

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

Cite this

On kernel fuzzy c-means for data with tolerance using explicit mapping for kernel data analysis. / Kanzawa, Yuchi; Endo, Yasunori; Miyamoto, Sadaaki.

In: Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 16, No. 1, 01.2012, p. 162-168.

Research output: Contribution to journalArticle

@article{859b929607d844849a0dd5b3f139d985,
title = "On kernel fuzzy c-means for data with tolerance using explicit mapping for kernel data analysis",
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.",
keywords = "Explicit mapping, Fuzzy c-means, Kernel data analysis, Tolerance",
author = "Yuchi Kanzawa and Yasunori Endo and Sadaaki Miyamoto",
year = "2012",
month = "1",
language = "English",
volume = "16",
pages = "162--168",
journal = "Journal of Advanced Computational Intelligence and Intelligent Informatics",
issn = "1343-0130",
publisher = "Fuji Technology Press",
number = "1",

}

TY - JOUR

T1 - On kernel fuzzy c-means for data with tolerance using explicit mapping for kernel data analysis

AU - Kanzawa, Yuchi

AU - Endo, Yasunori

AU - Miyamoto, Sadaaki

PY - 2012/1

Y1 - 2012/1

N2 - 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.

AB - 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.

KW - Explicit mapping

KW - Fuzzy c-means

KW - Kernel data analysis

KW - Tolerance

UR - http://www.scopus.com/inward/record.url?scp=84856001608&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84856001608&partnerID=8YFLogxK

M3 - Article

VL - 16

SP - 162

EP - 168

JO - Journal of Advanced Computational Intelligence and Intelligent Informatics

JF - Journal of Advanced Computational Intelligence and Intelligent Informatics

SN - 1343-0130

IS - 1

ER -