The improvement of classification accuracy with denoising class autoencoder

摘要

Denoising autoencoder is a data compression algorithm in deep learning, and it can successfully extract features which contain main information of the input data. However, this method is trained in an unsupervised manner and cannot aim at extracting the specified information. Therefore, the input data will be classified into a class label by main but not necessary information using traditional denoising autoencoder, and redundant information reduces the classification accuracy. To solve this problem, denoising class autoencoder presented in this paper is adopted to train a classification function and extract class features. Both the proposed approach and traditional methods are trained on MNIST handwritten digit database with mini-batch gradient descent, moreover the recognition result of the proposed approach demonstrates the effectiveness in comparison with conventional methods.