However, the underlying data structure is often non-linear in practice, therefore the low-rankness assumption could be violated. To tackle this issue, we propose a novel method for matrix recovery in this paper, which could well handle the case where the target matrix is low-rank in an ...
However, the underlying data structure is often non-linear in practice, therefore the low-rankness assumption could be violated. To tackle this issue, we propose a novel method for matrix recovery in this paper, which could well handle the case where the target matrix is low-rank in an ...
Empty Cell Schatten-α norm [146,147], Rank-N [149] Empty Cell γ-norm [160] Empty Cell Heuristic Recovery – Empty Cell Log-sum heuristic (LHR) [58] Empty Cell Stochastic Optimization – Empty Cell Max-norm (MRMD) [118] fsparse() l0-norm [11] PCP/Modified PCP Spatial Coher...
This paper seeks an ef?cient method to determine the local order of the linear model implicitly. According to the theory of low-rank matrix completion and recovery, a method for performing single-image SR is proposed by formulating the reconstruction as the recovery of a low-rank matrix, ...
low rankresolutionscalesmoothingFunctional data analyses typically proceed by smoothing, followed by functional PCA. This paradigm implicitly assumes that rough variation is due to nuisance noise. Nevertheless, relevant functional features such as time-localised or short scale fluctuations may indeed be ...
low-rankness; massive MIMO; matrix completion; compressive sensing Graphical Abstract 1. Introduction Millimeter-wave (mmWave) wireless communications have drawn great attention in the industry and academia [1] thanks to the large bandwidth available in the 30–300 GHz band. To compensate for the...
With the development of STAP, a great number of methods have been developed to confront the performance deterioration stemming from finite training samples. Sparsity-recovery (SR)-STAP [7,8,9,10] can greatly improve the SCM with only a few or even one training sample, however, the computation...
Figure 2. Recovery of Spatial Weights Matrix (N = 50, T = 50): Specification 1. (a) Two-step Lasso; (b) Two-step post-Lasso; (c) Two-step post-Lasso with τ = 0 . 05 . Figure 2. Recovery of Spatial Weights Matrix (N = 50, T = 50): Specification 1. (a) Two-step La...
Unfortunately, it only focuses on the signal power and does not make use of the data structure. To overcome this drawback, we try to focus on the properties of data. Herein, the covariance matrix that can implicitly capture the second-order statistical characteristics of the received data in ...
Figure 2. Recovery of Spatial Weights Matrix (N = 50, T = 50): Specification 1. (a) Two-step Lasso; (b) Two-step post-Lasso; (c) Two-step post-Lasso with τ = 0 . 05 . Figure 2. Recovery of Spatial Weights Matrix (N = 50, T = 50): Specification 1. (a) Two-step La...