Learning Label-Specific Features for Multi-Dimensional Classification

In multi-dimensional classification (MDC), instances are associated with multiple class variables that are assumed in the output space, and each class variable corresponds to one heterogeneous class space and characterizes the objects' semantics from one dimension. Learning from MDC examples poses challenges due to the heterogeneity of class spaces, since the outputs from different class spaces are not directly comparable. Moreover, existing approaches often use identical data representation for all labels in a class, which may lead to suboptimal results as each label might be determined by its own specific characteristics. Critically, the inherent incomparability of raw heterogeneous labels prevents existing methods from effectively capturing label correlations, which are essential for guiding feature learning. In this paper, we propose a novel algorithm named LEAD, i.e., learning Label-spEcific feAtures for multi-Dimensional classification. LEAD first resolves label heterogeneity by transforming the original output space into a unified encoded label space through one-hot label encoding. This critical alignment enables explicit extraction of label correlations from the encoded space. To enhance the reliability of the estimation of label correlations, LEAD then leverages feature-space manifold structures via locally linear embedding, propagating labeling information across similar instances to counteract sparsity. Finally, LEAD jointly learns label-specific feature representations and constructs the classifier through sparse learning while incorporating label correlations. Experimental comparisons on fifteen datasets demonstrate that our proposed method outperforms state-of-the-art multi-dimensional classification methods. The code is available at https://github.com/ZhangZan-source/LEAD.