Mapping the Concept Landscape: Structural Perception of Global Distributions for Transparent Data Pruning

Existing data pruning methods predominantly rely on high-dimensional feature embeddings to measure sample importance. How-ever, these compressed vectors often obscure fine-grained semantic inter-actions, leading to suboptimal coverage of rare semantic concepts in thepruned subsets. In this paper, we propose Mapping the Concept Land-scape (MCL), a novel structural perception framework for transparentdata pruning. Instead of abstract embeddings, we represent each image-caption pair as an explicit sample-level graph comprising entities, events,and attributes. By integrating these individual graphs into a comprehen-sive dataset-level graph, we characterize the global distribution of seman-tic concepts and quantify their rarity across the entire corpus. Basedon this structured perception, we develop a greedy concept-coveragemaximization algorithm that iteratively selects samples to maximize themarginal gain of high-value, under-represented concepts. Experimentalresults on various benchmarks demonstrate that our method not onlyachieves superior pruning efficiency compared to state-of-the-art meth-ods but also provides a transparent and interpretable audit trail for theselection process.