Comparative Study on Image Classification Algorithm Based on Dimensionality Reduction Technologies and Convolutional Neural Networks

Abstract

As the convolutional neural network is widely used in digital image processing , natural language processing, etc, it is facing more and more challenges. The convolutional neural network is designed to be more complex, and the amount of data required to train convolutional neural network has also grown substantially, which leads to higher time cost and hardware requirements for training convolutional neural network. This paper puts forward a new solution in order to solve this problem . In this paper, the whole image classification task is regarded as a competition, each feature in the image is regarded as an individual contestant, the image dimensionality reduction technology used in the model is regarded as a preliminary contest, and the convolutional neural network in the model is regarded as a final. The image processed by dimensionality reduction technology is input to CNN(Convolutional Neural Network). In order to verify the idea of this paper, we design two convolutional neural network architectures : model 1 and model 2. From the experimental results in this paper, it can be seen that the accuracy of the model 1 training dataset decreases with the increase in dimensionality reduction and approaches to 86.508% of the accuracy of training dataset without dimensionality reduction; the accuracy of model 2 training dataset is not sensitive to the selection of dimensionality reduction range and dimensionality reduction method and fluctuates within 7.126% as a whole; the accuracy and time cost of the test dataset of model 1 and model 2 are greatly affected by the dimensionality reduction method and dimensionality reduction range.The final experimental results show that the loss of accuracy can be reduced to an acceptable range, and the dimensionality reduction operation is very meaningful for the reduction of time cost.