SparseSolutionstoLinearInverseProblems WithMultipleMeasurementVectors ShaneF.Cotter,Member,IEEE,BhaskarD.Rao,Fellow,IEEE,KjerstiEngan,Member,IEEE,and KennethKreutz-Delgado,SeniorMember,IEEE Abstract—Weaddresstheproblemoffindingsparsesolutionsto anunderdeterminedsystemofequationswhentherearemultiple ...
In recent years, deep learning (DL) based approaches have attracted interests of researchers to solve the sparse linear inverse problem by unfolding iterative algorithms as neural networks. Typically, research concerning DL assume a fixed number of network layers. However, it ignores a key character...
Linear inverse problem for inferring eruption source parameters from sparse ash deposit data as viewed from an atmospheric dispersion modeling perspectiveVolcanic ashAggregate falloutTotal grain-size distributionVertical ash mass distributionIll-posed problem...
Y 是输入数据矩阵,D 为学习的字典,X 为稀疏编码项,η和 μ 分别为权重,A 为线性变换矩阵(linear transformation matrix),H 为对应于 Y 的标签信息(label information)矩阵,C 为分类器参数,L 为关于 Y 和 D 的标签的 联合标签矩阵(joint label matrix)。例如,假设 Y=[y1...y4] ,D=[d1...d4],其...
You should NEVER be inverting a large sparse linear system. The result will generally not be sparse, so you will then gain nothing from the sparsity. All it does is then make the code run more slowly, and use more memory.
• Algorithms for linear inverse problems with joint sparsity constraints [9-10]. In the next chapter we provide a summary of the thesis. 2 Preface Acknowledgements. I would like to thank the Numerical Harmonic Analysis Group (NuHAG) at the University of Vienna, and especially Hans Feichtinger...
SQNSR utilizes linear search strategy and quasi-Newton step to the solve composite objective function for the sparse recovery problem. Since \ell _1-norm-regularized item is nonsmooth, smoothing technique is introduced to obtain an approximate smoothed function. The sufficient and necessary condition ...
The Bayesian estimation approach presented here allows for a probabilistic treatment of the sparse reconstruction problem and has its roots in machine learning and the recently introduced relevance vector machine algorithm for linear inverse problems. We formulate the Bayesian sparse reconstruction algorithm ...
However, as noted in [12], the optimization problem denoted by Eq. (14) is not convex, making it inefficient to employ conventional methods for linear optimization problems, such as LASSO [19, 20]. Hence, the fixed cut-off thresholding algorithm, introduced in [12], is used with minor ...
A partitioning problem on chordal graphs that arises in the solution of sparse triangular systems of equations on parallel computers is considered. Roughly... BW Peyton,A Pothen,X Yuan - 《Linear Algebra & Its Applications》 被引量: 16发表: 1993年 NON-LINEAR N-TERM APPROXIMATION BY NON-STATIO...