UM
Multi-Attribute Based Fuzzy C-means in Approximated Feature Space
Liu, Zhulin; Chen, C. L. Philip; Chen, Long; Zhoto, Jin; IEEE
2017
Conference Name2017 INTERNATIONAL CONFERENCE ON FUZZY THEORY AND ITS APPLICATIONS (IFUZZY)
Publication Place345 E 47TH ST, NEW YORK, NY 10017 USA
PublisherIEEE
AbstractRecently, clustering illustrates its importance in knowledge discovery. However, most of the considered algorithms are efficient only on those linear separable datasets. Although the kernel based methods perform better with non-linear separable ones, at the same time, they are suffered from the missing of priority knowledge of a suitable kernel. Two latest methods which approximate date to infinity dimensional kernel feature spaces are considered in this paper. Furthermore, new attribute multi kernel weighted fuzzy c-means are proposed by combining the randomly projected features. From the experiments shown in the simulation section, we conclude that we develop an improved version of attribute multi-kernel weighted fuzzy c-means, which achieves better accuracy.
KeywordFuzzy C-means Random Features Random Fourier Features maps Quasi-Monte Carlo Feature maps Weighted Multi-kernel Fuzzy C-means Maximum Entropy Method for weighted data
URLView the original
Indexed ByCPCI
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000428738000039
The Source to ArticleWOS
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Document TypeConference paper
CollectionUniversity of Macau
Recommended Citation
GB/T 7714
Liu, Zhulin,Chen, C. L. Philip,Chen, Long,et al. Multi-Attribute Based Fuzzy C-means in Approximated Feature Space[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2017.
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