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 language | English |
|---|---|
| Pages (from-to) | 162-168 |
| Number of pages | 7 |
| Journal | Journal of Advanced Computational Intelligence and Intelligent Informatics |
| Volume | 16 |
| Issue number | 1 |
| Publication status | Published - 2012 Jan |
Fingerprint
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
Kanzawa, Y., Endo, Y., & Miyamoto, S. (2012). On kernel fuzzy c-means for data with tolerance using explicit mapping for kernel data analysis. Journal of Advanced Computational Intelligence and Intelligent Informatics, 16(1), 162-168.