AI RESEARCH
Better Together: Cross and Joint Covariances Enhance Signal Detectability in Undersampled Data
arXiv CS.LG
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ArXi:2507.22207v2 Announce Type: replace-cross Many data-science applications involve detecting a shared signal between two high-dimensional variables. Using random matrix theory methods, we determine when such signal can be detected and reconstructed from sample correlations, despite the background of sampling noise induced correlations.