arXiv stat.ML
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Sparse corruption in low-rank matrix inference: the PCA benchmark
arXiv:2511.11927v2 Announce Type: replace Abstract: Principal Component Analysis (PCA) is a standard tool for extracting a low-rank signal from noisy observations. It is known that applying PCA to a rank-one signal corrupted by a dense, homogeneous noise, in the large matrix size limit, the celebrated BBP transition oc