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Fundamental limits for rank-one matrix estimation with groupwise heteroskedasticity
[article]
2022
arXiv
pre-print
Low-rank matrix recovery problems involving high-dimensional and heterogeneous data appear in applications throughout statistics and machine learning. The contribution of this paper is to establish the fundamental limits of recovery for a broad class of these problems. In particular, we study the problem of estimating a rank-one matrix from Gaussian observations where different blocks of the matrix are observed under different noise levels. In the setting where the number of blocks is fixed
arXiv:2106.11950v2
fatcat:vakodsqyanbm5ftdkfjvvf4cji