AI RESEARCH
Does Dimensionality Reduction via Random Projections Preserve Landscape Features?
arXiv CS.LG
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ArXi:2604.13230v1 Announce Type: new Exploratory Landscape Analysis (ELA) provides numerical features for characterizing black-box optimization problems. In high-dimensional settings, however, ELA suffers from sparsity effects, high estimator variance, and the prohibitive cost of computing several feature classes. Dimensionality reduction has. therefore. been proposed as a way to make ELA applicable in such settings, but it remains unclear whether features computed in reduced spaces still reflect intrinsic properties of the original landscape.