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

Yuchi Kanzawa, Yasunori Endo, Sadaaki Miyamoto

研究成果: Article査読

抄録

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.

本文言語English
ページ(範囲)162-168
ページ数7
ジャーナルJournal of Advanced Computational Intelligence and Intelligent Informatics
16
1
DOI
出版ステータスPublished - 2012 1

ASJC Scopus subject areas

  • 人間とコンピュータの相互作用
  • コンピュータ ビジョンおよびパターン認識
  • 人工知能

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