Abstract:Deep learning is a kind of machine learning method widely used in the field of artificial intelligence. The high dependence of deep learning on data makes the dimension of the data needed to be processed, which greatly affects the computing efficiency and the performance of data classification. Taking data dimensionality as the research goal, the methods of dimensionality reduction in deep learning are analyzed in this paper. Then, taking Caltech 101 image dataset as experimental object, VGG-16 depth convolution neural network is used to extract image features, and PCA statistical method is taken as an example to achieve dimensionality reduction of high-dimensional image feature data. Euclidean distance is used as a similarity measure to test the accuracy index after dimensionality reduction at the testing stage. The experiments show that image can still maintain high feature information using the PCA method to reduce the data dimension to 64 dimensions after extracting the 4096 dimensional feature of the fc3 layer of the VGG-16 neural network.