Robust fuzzy clustering neural network based on epsilon-insensitive loss function
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 einsensitive loss function and quadratic programming optimization. Our experimental results demonstrate that RFCNN has much better robustness for outliers than FCNN. (c) 2006 Elsevier B. V. All rights reserved.
robustness fuzzy clustering neural networks epsilon-insensitive loss function outliers