Low-Rank Matrix Recovery

A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery
A primal-dual based framework for analyzing the global optimality of nonconvex low-rank matrix recovery.
Xiao Zhang, Lingxiao Wang, Yaodong Yu, Quanquan Gu
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Fast and Sample Efficient Inductive Matrix Completion via Multi-Phase Procrustes Flow
We present a new gradient-based optimization algorithm for inductive matrix completion, which achieves both linear rate of convengence and sample complexities linearly depending on the feature dimension.
Xiao Zhang, Simon Du, Quanquan Gu
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Fast and Sample Efficient Inductive Matrix Completion via Multi-Phase Procrustes Flow
A Unified Framework for Nonconvex Low-rank plus Sparse Matrix Recovery
A unified framework for solving general low-rank plus sparse matrix recovery problems.
Xiao Zhang, Lingxiao Wang, Quanquan Gu
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A Unified Framework for Nonconvex Low-rank plus Sparse Matrix Recovery
A Unified Variance Reduction-Based Framework for Nonconvex Low-Rank Matrix Recovery
A generic framework based on a new stochastic variance-reduced gradient descent algorithm for accelerating nonconvex low-rank matrix recovery
Lingxiao Wang, Xiao Zhang, Quanquan Gu
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A Unified Computational and Statistical Framework for Nonconvex Low-rank Matrix Estimation
A unified framework for estimating low-rank matrices through nonconvex optimization based on gradient descent
Lingxiao Wang, Xiao Zhang, Quanquan Gu
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