low rank matrix estimationmutual informationminimal mean squared errorspin glassesWe study low-rank matrix estimation for a generic inhomogeneous output channel through which the matrix is observed. This generalizes the commonly considered spiked matrix model with homogeneous noise to include for instance ...
As adirect corollary, we show both upper bounds and minimax lower bounds ofestimation accuracy under Ky-Fan-k norms for every $1\\\leq k\\\leq m$.doi:10.48550/arXiv.1403.6499Xia, DongD. Xia. Optimal schatten-q and ky-fan-k norm rate of low rank matrix estimation. arXiv preprint ar...
Chen, Y., Wainwright, M.J.: Fast low-rank estimation by projected gradient descent: General statistical and algorithmic guarantees.arXiv:1509.03025(2015) Chi, Y., Lu, Y., Chen, Y.: Nonconvex optimization meets low-rank matrix factorization: An overview.arXiv:1809.09573(2018) ...
Fundamental limits of low-rank matrix estimation: the non-symmetric case We consider the high-dimensional inference problem where the signal is a low-rank matrix which is corrupted by an additive Gaussian noise. Given a probabilistic model for the low-rank matrix, we compute the limit in the ...
news and stories from top researchers in related subjects. artificial intelligence use our pre-submission checklist avoid common mistakes on your manuscript. 1 introduction low rank matrix estimation has broad applications in machine learning, computer vision and signal processing. in this paper, we con...
Low-rank matrix estimation in model (1.1) has been extensively studied in the setting where the additive noise matrix W has Gaussian entries with homoscedastic variance (Shabalin and Nobel, 2013; Candès et al., 2013; Donoho and Gavish, 2014; Nadakuditi, 2014). However, in many situations,...
In this study, a low-rank matrix estimation-based spatiotemporal image reconstruction (LRME-STIR) method is investigated for dynamic PACT applications. The LRME-STIR method is based on the observation that, in many PACT applications, the number of frames is much greater than the rank of the ...
Fast Low-Rank Matrix Estimation without the Condition Number In this paper, we study the general problem of optimizing a convex function F(L) over the set of p×p matrices, subject to rank constraints on L. However, existing first-order methods for solving such problems either are too slow...
Inspired by above evidence, we propose a novel Uncertainty-Aware Low-rank Q-matrix Estimation (UA-LQE) algorithm as a general framework to facilitate the learning of value function. Through quantifying the uncertainty of state-action value estimation, we selectively erase the entries of highly ...
Luo, X. (2013), `Recovering model structures from large low rank and sparse covari- ance matrix estimation', arXiv preprint arXiv:1111.1133 .X. Luo, Recovering model structures from large low rank and sparse co- variance matrix estimation, arXiv preprint arXiv:1111.1133. [25] H. Liu, J...