Abstract
An explicit mapping is generally unknown for kernel data analysis but their inner product should be known. Though kernel fuzzy c-means algorithm for data with tolerance has been proposed by the authors, the cluster centers and the tolerance in higher dimensional space have been unseen. Contrary to this common assumption, an explicit mapping has been introduced by one of the authors and the situation of kernel fuzzy c-means in higher dimensional space has been described via kernel principal component analysis using the explicit mapping. In this paper, the cluster centers and the tolerance of kernel fuzzy c-means for data with tolerance are described via kernel principal component analysis using the explicit mapping.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
