Robust fuzzy clustering neural network based on ε-insensitive loss function

Authors:
Shitong Wang;Korris F. L. Chung;Deng Zhaohong;Hu Dewen
Affiliations:
School of Information, Southern Yangtse University, WuXi, Jiang Su, China;Department of Computing, Hong Kong Polytechnic University, Hong Kong;School of Information, Southern Yangtse University, WuXi, Jiang Su, China and Department of Computing, Hong Kong Polytechnic University, Hong Kong and National Keysoft Lab., Nanjing University, Na ...;School of Information, Southern Yangtse University, WuXi, Jiang Su, China and School of Automation, National Defense University of Science and Technology, China
Venue:
Applied Soft Computing
Year:
2007

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Abstract

In the paper, as an improvement of fuzzy clustering neural network FCNN proposed by Zhang et al., a novel robust fuzzy clustering neural network RFCNN is presented to cope with the sensitive issue of clustering when outliers exist. This new algorithm is based on Vapnik's @?-insensitive loss function and quadratic programming optimization. Our experimental results demonstrate that RFCNN has much better robustness for outliers than FCNN.