关键词: 主成分分析, 自编码机, 卷积神经网络, 迁移学习

Abstract:

Aiming at the problem of low-precision and high-cost of the traditional convolutional neural network when the training set and test set have different distribution, convolutional neural network algorithm combined with transfer learning model is proposed. Firstly, using the principal component analysis algorithm to conduct unsupervised dimension reduction of the source data, meanwhile, the auto-encoder algorithm is combined to reduce the dimension of the target dataset in order to make the source domain and the target dataset have the similar distribution of features in the low dimension. Secondly, according to the characteristics of the feature extraction of convolutional neural network, JS divergence is used to determine whether convolution pool layer can be migrated and initialized hidden layer is used to complete the model of trCNN. Finally, a small number of the marked target datasets are used to train to finish the building of the classification model. The design experiment verifies that the classification model can accurately perform the classification work with a small amount of annotation data.

Key words: principal component analysis, auto-encoder, convolutional neural network, transfer learning