To address the feature sparseness problem encountered in short-text and cross-domain classification tasks, we propose a novel method that computes related features that can be appended to the feature vectors to reduce the sparsity.
we decompose a feature-relatedness graph into core-periphery (CP) structures, where a core feature (a vertex) is linked to a set of peripheries (also represented by vertices), indicating the connectivity of the graph.