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• W3088764569 abstract "We study the problem of learning mixtures of low-rank models, i.e. reconstructing multiple low-rank matrices from unlabelled linear measurements of each. This problem enriches two widely studied settings -- low-rank matrix sensing and mixed linear regression -- by bringing latent variables (i.e. unknown labels) and structural priors (i.e. low-rank structures) into consideration. To cope with the non-convexity issues arising from unlabelled heterogeneous data and low-complexity structure, we develop a three-stage meta-algorithm that is guaranteed to recover the unknown matrices with near-optimal sample and computational complexities under Gaussian designs. In addition, the proposed algorithm is provably stable against random noise. We complement the theoretical studies with empirical evidence that confirms the efficacy of our algorithm." @default.
• W3088764569 created "2020-10-01" @default.
• W3088764569 creator A5042307561 @default.
• W3088764569 creator A5061140388 @default.
• W3088764569 creator A5074490643 @default.
• W3088764569 creator A5080585531 @default.
• W3088764569 date "2020-09-23" @default.
• W3088764569 modified "2023-10-16" @default.
• W3088764569 title "Learning Mixtures of Low-Rank Models" @default.
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