Sparse semi-autoencoders to solve the vanishing information problem in multi-layered neural networks

• Published in: Applied intelligence (Dordrecht, Netherlands) Netherlands), 2019-07, Vol.49 (7), p.2522-2545
• Main Authors: Kamimura, Ryotaro, Takeuchi, Haruhiko
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Computer Science; Data processing; Datasets; Economic models; Errors; Fines & penalties; Machines; Manufacturing; Mechanical Engineering; Methods; Multilayers; Neural networks; Optimization; Processes; Pruning; Reduction; Regularization; Selectivity; Tracking; Training
• ISSN: 0924-669X; 1573-7497
• DOI: 10.1007/s10489-018-1393-x

The present paper aims to propose a new neural network called “sparse semi-autoencoder” to overcome the vanishing information problem inherent to multi-layered neural networks. The vanishing information problem represents a natural tendency of multi-layered neural networks to lose information in input patterns as well as training errors, including also natural reduction in information due to constraints such as sparse regularization. To overcome this problem, two methods are proposed here, namely, input information enhancement by semi-autoencoders and the separation of error minimization and sparse regularization by soft pruning. First, we try to enhance information in input patterns to prevent the information from decreasing when going through multi-layers. The information enhancement is realized in a form of new architecture called “semi-autoencoders”, in which information in input patterns is forced to be given to all hidden layers to keep the original information in input patterns as much as possible. Second, information reduction by the sparse regularization is separated from a process of information acquisition as error minimization. The sparse regularization is usually applied in training autoencoders, and it has a natural tendency to decrease information by restricting the information capacity. This information reduction in terms of the penalties tends to eliminate even necessary and important information, because of the existence of many parameters to harmonize the penalties with error minimization. Thus, we introduce a new method of soft pruning, where information acquisition of error minimization and information reduction of sparse regularization are separately applied without a drastic change in connection weights, as is the case of the pruning methods. The two methods of information enhancement and soft pruning try jointly to keep the original information as much as possible and particularly to keep necessary and important information by enabling the making of a flexible compromise between information acquisition and reduction. The method was applied to the artificial data set, eye-tracking data set, and rebel forces participation data set. With the artificial data set, we demonstrated that the selectivity of connection weights increased by the soft pruning, giving sparse weights, and the final weights were naturally interpreted. Then, when it was applied to the real data set of eye tracking, it was confirmed that the present method outperform