Auto-Associative Neural Network System for Recognition
Xianhua Zeng, Siwei Luo, Jiao Wang · 2007
Recently, a nonlinear dimension reduction technique, called Autoencoder, had been proposed. It can efficiently carry out mappings in both directions between the original data and low-dimensional code space. However, a single Autoencoder commonly maps all data into a single subspace. If the original data set have remarkable different categories (for example, characters and handwritten digits), then only one Autoencoder will not be efficient. To deal with the data of remarkable different categories, this paper proposes an auto-associative neural network system (AANNS) based on multiple Autoencoders. The novel technique has the functions of auto-association, incremental learning and local update. Excitingly, these functions are the foundations of cognitive science. Experimental results on benchmark MNIST digit dataset and handwritten character-digit dataset show the advantages of the proposed model.