Fully Automated Deep Residual PCA Network

Abstract

Recently, a deep residual form of the principal component analysis (PCA) model has been proposed as a feature engineering for industrial data analytics, which has obtained more satisfactory performances compared to the shallow feature engineering model. However, a critical issue remain unsolved is how to effectively determine the number of hidden layers in the deep model, which may significantly influence its performance. In this article, a novel hidden layer selection strategy is proposed to automate the training process of the deep residual PCA model. With a new definition of similarity factor based on cosine distance between two latent variables, the degree of pattern repetition can be well recognized and evaluated. In addition, a layer retained factor is further defined to assess the necessity of adding a new hidden layer to the deep model. As a result, the number of required hidden layers can be automatically determined, making the deep residual PCA model fully automated. Four industrial case studies are provided for performance evaluation, based on which both feasibility and effectiveness of the new strategy are confirmed.