Correntropy-based Fusion Extreme Learning Machine forRepresentation Level Feature Fusion and Classification
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摘要:
在极限学习机(ELM)网络结构和训练模式的基础上，该文提出了相关熵融合极限学习机(CF-ELM)。针对多数分类方法中表示级特征融合不充分的问题，该文将核映射与系数加权相结合，提出了能够有效融合表示级特征的融合极限学习机(F-ELM)。在此基础上，用相关熵损失函数替代均方误差(MSE)损失函数，推导出用于训练F-ELM各层权重矩阵的相关熵循环更新公式，以增强其分类能力与鲁棒性。为了检验方法的可行性，该文分别在数据库Caltech 101, MSRC和15 Scene上进行实验。实验结果证明，该文所提CF-ELM能够在原有基础上进一步融合表示级特征，从而提高分类正确率。
Abstract: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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表 1 Caltech 101与MSRC的正确率与训练时间
组合方法 SVM KELM F-ELM CF-ELM 组合1 组合2 组合3 组合1 组合2 组合3 组合1 组合2 组合3 组合1 组合2 组合3 Caltech 101 (%) 72.04 77.93 – 79.43 78.84 – 80.31 80.19 – 80.59 83.65 – 训练时间(s) 862.16 540.30 – 860.37 538.56 – 861.76 539.77 – 902.13 569.80 – MSRC (%) 90.26 88.57 94.13 91.42 90.42 91.74 91.74 90.58 93.49 90.95 92.06 95.76 训练时间(s) 79.59 17.11 17.11 79.51 17.01 17.01 79.54 17.04 17.04 80.68 18.20 18.20 
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表 3 15 Scene的正确率与训练时间
组合方法 SVM KELM F-ELM CF-ELM 组合1 组合2 组合4 组合1 组合2 组合4 组合1 组合2 组合4 组合1 组合2 组合4 15 Scene(%) 74.34 77.92 86.46 72.00 80.73 83.53 77.20 82.06 84.33 79.00 83.06 87.76 训练时间(s) 347.44 106.25 106.25 347.12 106.11 106.11 347.37 106.41 106.41 367.50 120.10 120.10
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