关键词: 主成分分析, 卷积神经网络, 卷积核, 手写数字识别

Abstract: On the issues about the slow convergence speed and low identification rate in handwritten digit recognition, based on CNN（Convolutional Neural Network） in which the convolution kernels are initialized randomly, an improved algorithm in which the convolution kernels are initialized by PCA（Principal Component Analysis） is proposed. Firstly, training samples are selected and sent to CNN. In the corresponding layer feature map is processed by image block extraction, after that feature vectors extracted by the way of PCA in layered learning are used to initialize the convolution kernels. Finally, original images of the CNN are processed by these convolution kernels. Compared with the CNN handwritten digit recognition algorithm that randomly initializes the convolution kernel, the improved algorithm not only converges when applied to the MNIST database training, but also has fewer iterations and higher recognition rate when the same mean square error is generated.

Key words: Principal Component Analysis（PCA）, Convolutional Neural Network（CNN）, convolution kernel, handwritten digit recognition