Incomplete multi-view partial multi-label learning

Partial multi-label learning is of great significant interest due to accurate supervision is difficult to be obtained. Recently, multi-view learning has been developed to deal with partial multi-label learning tasks. Although few multi-view partial multi-label learning methods have been proposed, all of them are designed under the full-view assumption. However, due to the difficulties in multi-view data collection, some views may not contain complete information in real task. The appearance of missing views will affect the performance of traditional partial multi-label learning algorithms. To solve this problem, we propose a novel I ncomplete M ulti-V iew P artial M ulti-L abel learning (IMVPML) framework which makes use of incomplete multi-view feature representation and utilizes the low-rank and sparse decomposition scheme to remove the noisy labels. Specifically, we first learn a shared subspace across heterogenous incomplete views. Secondly, we utilize the low-rank and sparse decomposition scheme to obtain the ground-truth labels. Thirdly, we introduce a graph Laplacian regularization to constrain the ground-truth labels and impose orthogonality constraints on the correlations between subspace. Finally, a predictive model is learned by shared subspace and disambiguation labels. Enormous experimental results demonstrate that the proposed method can achieve competitive performance in solving the problem of incomplete multi-view partial multi-label learning.