We propose a unified framework for estimating low-rank matrices through nonconvex optimization based on gradient descent algorithm. Our framework is quite general and can be applied to both noisy and noiseless observations. In the general case with noisy observations, we show that our algorithm is guaranteed to linearly converge to the unknown low-rank matrix up to minimax optimal statistical error, provided an appropriate initial estimator. While in the generic noiseless setting, our algorithm converges to the unknown low-rank matrix at a linear rate and enables exact recovery with optimal sample complexity. In addition, we develop a new initialization algorithm to provide a desired initial estimator, which outperforms existing initialization algorithms for nonconvex low-rank matrix estimation. We illustrate the superiority of our framework through three examples: matrix regression, matrix completion, and one-bit matrix completion. We also corroborate our theory through extensive experiments on synthetic data.
REF proposed a unified framework for nonconvex low-rank matrix estimation under the restricted strongly convex and smooth conditions, which covers both noisy and noiseless matrix sensing as special cases.
A Unified Computational and Statistical Framework for Nonconvex Low-Rank Matrix Estimation
AISTATS 2017
arXiv: Machine Learning
Mathematics Computer Science