基于随机特征映射的四层多核学习方法

针对单核网络模型的核函数选择无理论依据以及基于随机特征映射的四层神经网络(FRMFNN)节点规模过大的问题,提出了一种基于随机特征映射的四层多核学习神经网络(MK-FRMFNN)算法.首先,把原始输入特征通过特定的随机映射算法转化为随机映射特征;然后,经过不同的随机核映射生成多个基本核矩阵;最后,将基本核矩阵组成合成核...

Four-layer multiple kernel learning method based on random feature mapping

Since there is no perfect theoretical basis for the selection of kernel function in single kernel network models， and the network node size of Four-layer Neural Network based on Randomly Feature Mapping （FRFMNN） is excessively large， a Four-layer Multiple Kernel Neural Network based on Randomly Feature Mapping （MK-FRFMNN） algorithm was proposed. Firstly， the original input features were transformed into randomly mapped features by a specific random mapping algorithm. Then， multiple basic kernel matrices were generated through different random kernel mappings. Finally， the synthetic kernel matrix formed by basic kernel matrices was linked to the output layer through the output weights. Since the weights of random mapping of original features were randomly generated according to the random continuous sampling probability distribution randomly， without the need of updates of the weights， and the weights of the output layer were quickly solved by the ridge regression pseudo inverse algorithm， thus avoiding the time-consuming training process of the repeated iterations. Different random weight matrices were introduced into the basic kernel mapping of MK-FRFMNN. the generated synthetic kernel matrix was able to not only synthesize the advantages of various kernel functions， but also integrate the characteristics of various random distribution functions， to obtain better feature selection and expression effect in the new feature space. Theoretical and experimental analyses show that， compared with the single kernel models such as Broad Learning System （BLS） and FRMFNN， MK-FRMFNN model has the node size reduced by about 2/3 with stable classification performance； compared with mainstream multiple kernel models， MK-FRMFNN model can learn large sample datasets， and has better performance in classification.