Abstract Incomplete view information often results in failure cases of the conventional multi-view methods. To address this problem, we propose a Deep Correlated Predictive Subspace Learning (DCPSL) method for incomplete multi-view semisupervised classifification. Specififically, we integrate semi-supervised deep matrix factorization, correlated subspace learning, and multi-view label prediction into a unifified framework to jointly learn the deep correlated predictive subspace and multiview shared and private label predictors. DCPSL is able to learn proper subspace representation that is suitable for class label prediction, which can further improve the performance of classifification. Extensive experimental results on various practical datasets demonstrate that the proposed method performs favorably against the state-of-the-art methods