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Sep, 2023
小随机梯度下降的非对称矩阵感知
Asymmetric matrix sensing by gradient descent with small random initialization
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Johan S. Wind
TL;DR
矩阵感知是从少量线性测量中重建低秩矩阵的问题,我们引入了连续微分方程,称其为“扰动梯度流”,通过边界足够有界的累计误差,证明扰动梯度流迅速收敛到真实目标矩阵,从而提供了一种基于梯度下降的非对称矩阵感知的新证明方法。
Abstract
We study
matrix sensing
, which is the problem of reconstructing a low-rank matrix from a few linear measurements. It can be formulated as an overparameterized regression problem, which can be solved by
factorized gradie
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