Entrywise Estimation of Singular Vectors of Low-Rank Matrices with Heteroskedasticity and Dependence
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DOI: 10.1109/tit.2022.3159085
Abstract: We propose an estimator for the singular vectors of high-dimensional low-rank matrices corrupted by additive subgaussian noise, where the noise matrix is allowed to have dependence within rows and heteroskedasticity between them. We prove finite-sample… read more here.
Keywords: noise; dependence; singular vectors; rank matrices ... See more keywords