A Supervised Variational Autoencoder for Incomplete Multi‐View Classification

ABSTRACT Although significant progress has been made in multi‐view classification over the past few decades, handling multi‐view data with arbitrary view missing is still a challenge. To address the challenge of incomplete multi‐view classification, we propose a novel framework named Supervised Variational Incomplete Multi‐View Classification (SVIMC) network, which completes incomplete multi‐view data and performs classification predictions. Specifically, we design a supervised multi‐view variational autoencoder for missing view completion, which involves a Product of Experts (PoE) network to obtain the latent joint representation of available views. This representation is then fed into the view‐specific decoders of the missing views to generate the imputations. Besides, we jointly optimise the classification network and the missing view completion module, allowing them to mutually promote each other. Moreover, by adopting the GradNorm method, we significantly reduce the difficulty of model training. Extensive experiments have been conducted to demonstrate the effectiveness of our method in terms of classification accuracy, missing view imputation visualisation and ablation study.