Instance structure based multi-label learning with missing labels - Tianzhu CHEN; Fenghua LI; Yunchuan GUO; Zifu LI (Tongxin xuebao)
To address the problem that the existing methods in multi-label learning did not efficiently deal with the problems, the instance structure based multi-label learning scheme with missing labels was proposed.By considering the feature and label structure of instance, the similarity of label vectors were exploit to fill the missing labels and the weight rank loss was exploit to reduce the model bias.Meanwhile, the weight rank loss was also exploit to reduce the model bias.More specially, the manifold structure was capture by forcing the consistency of the geometry similarity of labels and one of the predicted labels.By measuring ranking loss for complete labels and incomplete labels, the relevance of label was distinguish to instance.Experiment results show that the superior performances of the proposed approach compared with the state-of-the-art methods and the accuracy is improved by more than 10% compared with the best comparison scheme under some evaluation criteria.