Robust non-negative sparse graph for semi-supervised multi-label learning with missing labels

摘要

Highlights?A novel label recovery method based on semi-supervised learning is proposed.?The proposed semi-supervised method learns the label matrix for labeled data imputation and unlabeled data prediction.?We used relational graphs to facilitate the label recovery.?We introduced sparse and nonnegative constraints to enhance multi-label optimization.?The proposed semi-supervised method is thoroughly tested by 5 benchmark multi-label datasets.AbstractIn multi-label learning, each instance is assumed to belong to multiple nonexclusive classes among a finite number of candidate categories. Labels are related to certain conceptual space according to their semantic similarities. Most existing approaches that deal with missing labels have the limitations i