Semi-supervised partial multi-label classification via consistency learning

Highlights：

• We solve the inconsistency of the distributions in features and labels and acquire the label level instance correlation via HSIC for partial multi-label datasets in semi-supervised scenarios.

• We propose a semi-supervised partial multi-label method inability of training the feature mapping, recovering ground-truth labels and alleviating noisy labels, and we also derive a nonlinear version of the proposed method.

• We confirm empirically the effectiveness of the proposed methods, and we further verify the importance of the HISC technique for label-level instance correlation estimation on unseen instances.