| Citation: | Chao WU, Yaqian LI, Yaru ZHANG, Bin LIU. Correntropy-based Fusion Extreme Learning Machine forRepresentation Level Feature Fusion and Classification[J]. Journal of Electronics & Information Technology, 2020, 42(2): 386-393. doi: 10.11999/JEIT190186
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Based on the network structure and training methods of the Extreme Learning Machine (ELM), Correntropy-based Fusion Extreme Learning Machine (CF-ELM) is proposed. Considering the problem that the fusion of representation level features is insufficient in most classification methods, the kernel mapping and coefficient weighting are combined to propose a Fusion Extreme Learning Machine (F-ELM), which can effectively fuse the representation level features. On this basis, the Mean Square Error (MSE) loss function is replaced by the correntropy-based loss function. A correntropy-based cycle update formula for training the weight matrices of the F-ELM is derived to enhance classification ability and robustness. Extensive experiments are performed on Caltech 101, MSRC and 15 Scene datasets respectively. The experimental results show that CF-ELM can further fuse the representation level features to improve the classification accuracy.
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