Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (1): 16-25.DOI: 10.11772/j.issn.1001-9081.2021010171

• Artificial intelligence • Previous Articles     Next Articles

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

Yue YANG(), Shitong WANG   

  1. School of Artificial Intelligence and Computer Science,Jiangnan University,Wuxi Jiangsu 214122,China
  • Received:2021-01-29 Revised:2021-04-24 Accepted:2021-05-10 Online:2021-06-04 Published:2022-01-10
  • Contact: Yue YANG
  • About author:YANG Yue, born in 1995, M. S. candidate. Her research interests include machine learning, neural network.
    WANG Shitong, born in 1964, M. S., professor. His research interests include artificial intelligence, pattern recognition.
  • Supported by:
    Natural Science Foundation of Jiangsu Province(BK20191331)


杨悦(), 王士同   

  1. 江南大学 人工智能与计算机学院,江苏 无锡 214122
  • 通讯作者: 杨悦
  • 作者简介:杨悦(1995—),女,云南玉溪人,硕士研究生,主要研究方向:机器学习、神经网络
  • 基金资助:


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.

Key words: random feature mapping, sparse autoencoder, multiple kernel learning, ridge regression, regularization



关键词: 随机特征映射, 稀疏自动编码器, 多核学习, 岭回归, 正则化

CLC Number: