low rank matrix approximationmodel selectionnuclear norm penalizationreduced rank regressionStein's unbiased risk estimatorThe objective of this paper is to quantify the complexity of rank and nuclear norm constrained methods for low rank matrix estimation problems. Specifically, we derive analytic forms ...
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...
Towards faster rates and oracle property for low-rank matrix estimation. arXiv preprint arXiv:1505.04780, 2015.H. Gui, J. Han, and Q. Gu. Towards faster rates and oracle property for low-rank matrix estimation. In Proceedings of the 33nd International Conference on Machine Learning, pages ...
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 ...
Oboué YA, Chen W, Saad OM et al (2023) Adaptive damped rank-reduction method for random noise attenuation of three-dimensional seismic data. Surv Geophys 44(3):847–875 Article Google Scholar Oboué YASI, Chen Y (2021) Enhanced low-rank matrix estimation for simultaneous denoising and rec...
Simultaneously Sparse and Low-Rank Abundance Matrix Estimation for Hyperspectral Image Unmixing 来自 国家科技图书文献中心 喜欢 0 阅读量: 64 作者:PV Giampouras,KE Themelis,AA Rontogiannis,KD Koutroumbas 摘要: In a plethora of applications dealing with inverse problems, e.g., image processing, ...
网络释义 1. 低秩逼近 低秩,low... ... ) low-rank order 低阶秩 )low-rank approximation低秩逼近) low-rank 低秩 ... www.dictall.com|基于3个网页 2. 低阶近似 ... ) fifth-order approximationH 五阶近似 )low-rank approximation低阶近似) low dimensional approximation 低阶近似 ... ...
Recovery of low-rank matrices has recently seen significant activity in many areas of science and engineering, motivated by recent theoretical results for exact reconstruction guarantees and interesting practical applications. A number of methods have be
In this paper, L1 and nuclear norm regularization are applied simultaneously to obtain a more robust eigenphone estimation, resulting in a sparse and low-rank eigenphone matrix. The sparse constraint can reduce the number of free parameters while the low rank constraint can limit the dimension of...
X. Luo, Recovering model structures from large low rank and sparse co- variance matrix estimation, arXiv preprint arXiv:1111.1133. [25] H. Liu, J. Lafferty, L. Wasserman, The nonparanormal: Semiparametric estimation of high dimensional undirected graphs, The Journal of Machine Learning Research...