Low-rank and sparse recoveryBilinear compressed sensingMulti-penalty regularizationIterative soft-thresholding (LASSO)We consider the problem of recovering an unknown effectively (s_1,s_2) (s_1,s_2) -sparse low-rank- R R matrix X X with possibly non-orthogonal rank- 1 1 decomposition from ...
such that S(X) = N i=1 X i . B. Tensor recovery problems The goal of tensor recovery is to find a tensor X ∈ T satisfying A(X) = b, where b ∈ R相关精品文档 更多 (2013pami)Low-Rank Matrix Approximation with Manifold Regularization Robust Video Restoration by Joint Sparse and...
A Shrinkage Principle for Heavy-Tailed Data: High-Dimensional Robust Low-Rank Matrix Recovery 来自 Semantic Scholar 喜欢 0 阅读量: 128 作者:J Fan,W Wang,Z Zhu 摘要: This paper introduces a simple principle for robust high-dimensional statistical inference via an appropriate shrinkage on the data...
Robust Video Restoration by Joint Sparse and Low Rank Matrix… 热度: resource efficient low power laser cleaning of compact discs for material reuse by polycarbonate recovery:利用聚碳酸酯回收材料回收材料的高效低功率激光清洗 热度: 1 RobustRecoveryofSubspaceStructuresby ...
Low-rank matrix recovery with structural incoherence for robust face recognition We address the problem of robust face recognition, in which both training and test image data might be corrupted due to occlusion and disguise. From standa... CF Chen,CP Wei,YCF Wang - Computer Vision & Pattern ...
Usually, the observed data matrix its... G Liu,S Yan - International Conference on Computer Vision 被引量: 401发表: 2011年 Exact Subspace Segmentation and Outlier Detection by Low-Rank Representation In this work we address the subspace recovery problem. Given a set of data samples (vectors)...
Usually, the observed data matrix its... G Liu,S Yan - International Conference on Computer Vision 被引量: 401发表: 2011年 Exact Subspace Segmentation and Outlier Detection by Low-Rank Representation In this work we address the subspace recovery problem. Given a set of data samples (vectors)...
Also, the matrix ‘1 and ‘2;1 norms are good relaxations of the ‘0 and ‘2;0 norms, respectively. So we could obtain a low-rank recovery to X0 by solving the following convex optimization problem: min kZ k? ? kE k2;1 ; Z;E 4.3 s:t: X ? AZ ? E: ?7? is the unique...
{em non-convex} but easy to compute. In spite of this non-convexity, we establish exact recovery of the low-rank matrix, under the same conditions that are required by existing methods (which are based on convex optimization). For an $m imes n$ input matrix ($m leq n)$, our method...
Recent advances have shown that implicit bias of gradient descent on over-parameterized models enables the recovery of low-rank matrices from linear measurements, even with no prior knowledge on the intrinsic rank. In contrast, for robust low-rank matrix recovery from grossly corrupted measurements, ...