Dual Noise Elimination and Dynamic Label Correlation Guided Partial Multi-Label Learning

Partial multi-label learning (PML) needs to address the problem of multi-label learning when the dataset contains redundant information. PML is more challenging compared to traditional multi-label learning, because PML needs not only to perform the multi classification task, but also to reduce the impact of noise information on the model. Existing PML methods suffer from the following problems. (1) Only single source of noise is considered. (2) Some methods ignore the label correlations. To solve the above problems, we proposes a new dual noise elimination and dynamic label correlation guided partial multi-label learning (PML-DNDC). Specifically, the hidden ground-truth label matrix is decomposed into two compressed matrices of instance and classifier, which are used to approximate the candidate label matrix, to eliminate the negative effects of label noise on the model. On one hand, the compressed instance matrix maintains local structural consistency with the original instances, eliminating noise in the feature. On the other hand, dynamic label correlation guidance is designed to help classifier training by dynamically exploring the potential label correlations, which encourages relevant labels to obtain similar classifiers. After extensive experiments and analyses, we conclude that the proposed PML-DNDC is superior to the state-of-the-art methods.