“In real-world applications, … multi-label learning methods emerged in recent years. It is a more challenging problem for many reasons. … In general, overcoming these challenges and bettering learning performance could be achieved by utilizing more training samples and including label correlations. However, these solutions are expensive and inflexible. Large-scale, well-labeled datasets are difficult to obtain, and building label correlation maps requires task-specific semantic information as prior knowledge. To address these limitations, we propose a general and compact Multi-Label Correlation Learning (MUCO) framework.”

Find the paper and full list of authors at ACM Transactions on Knowledge Discovery from Data.