Multi-Label learning in the independent label sub-spaces

The objective in multi-label learning problems is simultaneous prediction of many labels for each input instance. During the past years, there were many proposed embedding based approaches to solve this problem by considering label dependencies and decreasing learning and prediction cost. However, compressing the data leads to lose part of information included in label space. The idea in this work is to divide the whole label space to some independent small groups which leads to independent learning and prediction for each small group in the main space, rather than transforming to the compressed space. We use subspace clustering approaches to extract these small partitions such that the labels in each group do not include any information to improve the results for the labels in the other groups. According to the experiments on different datasets with various number of features and labels, the approach improves prediction quality with lower computational cost compared to the state-of-the-art.