Imputation of continuous missing values in profile data

Abstract

Profile data have been widely used in quality control practice. However, in some applications, incomplete profiles, or profiles with continuous missing values, are frequently encountered. Imputation of continuous missing values in profiles for quality control is a challenging problem. In this work, we propose a latent feature model and a two-step learning algorithm to reconstruct missing values. In the model, an unsupervised deep-learning technique, variational autoencoder (VAE), is utilized to learn the latent features from profile data. Then a supervised variational coding (SVC) scheme is proposed to map the incomplete profile data to the learned latent features, and imputation is then made based on the latent features. The performance of the proposed method is evaluated by simulated and real datasets, and the results prove the effectiveness and superiority of the proposed method.