Big Data Image Classification Based on Distributed Deep Representation Learning Model

Traditional image classification technology has become increasingly unable to meet the changing needs of the era of big data. With the open source use of a large number of marked databases and the development and promotion of computers with high performance, deep learning has moved from theory to practice and has been widely used in image classification. This paper takes big data image classification as the research object, selects distributed deep learning tools based on Spark cluster platform, and studies the image classification algorithm based on distributed deep learning. Aiming at the problems that the Labeled Structural Deep Network Embedding (LSDNE) model is applied to the attribute network and generates a large number of hyperparameters and the model complexity is too high, inspired by the Locally Linear Embedding (LLE) algorithm, this paper proposes a semi-supervised network based on the neighbor structure learning model. This model will add the neighbor information of the node at the same time when learning the network representation. Through the node vector reconstruction, the node itself and the neighbor node together constitute the next layer of representation. On the basis of Structural Labeled Locally Deep Nonlinear Embedding (SLLDNE), the node attribute is further added to propose Structural Informed Locally Distributed Deep Nonlinear Embedding (SILDDNE), and how the model combines the structural characteristics of the node with the attribute characteristics is explained in detail. The SVM classifier classifies the known labels, and SILDDNE fuses the network structure, labels, and node attributes into the deep neural network. The experimental results on the CIFAR-10 and CIFAR-100 datasets for image classification standard recognition tasks show that the proposed network achieves good classification performance and has a high generalization ability. Experiments on the CIFAR-10 data set show that the 34-layer SLLDNE pruned 40-layer Dense Net compresses about 50% of the parameter amount, increases the computational complexity efficiency by about 8 times, and reduces the classification error rate by 30%. Experiments on the CIFAR-100 data set show that the 34-layer SLLDNE parameter volume is compressed by about 16 times compared to the 19-layer VGG parameter volume, the computational complexity efficiency is increased by about 6 times, and the classification error rate is reduced by 14%.