This paper considers approximately sparse signal and low-rank matrix’s recovery via truncated norm minimization minx∥xT∥q and minX∥XT∥Sq from noisy measurements.We first introduce truncated sparse approximation p...This paper considers approximately sparse signal and low-rank matrix’s recovery via truncated norm minimization minx∥xT∥q and minX∥XT∥Sq from noisy measurements.We first introduce truncated sparse approximation property,a more general robust null space property,and establish the stable recovery of signals and matrices under the truncated sparse approximation property.We also explore the relationship between the restricted isometry property and truncated sparse approximation property.And we also prove that if a measurement matrix A or linear map A satisfies truncated sparse approximation property of order k,then the first inequality in restricted isometry property of order k and of order 2k can hold for certain different constantsδk andδ2k,respectively.Last,we show that ifδs(k+|T^c|)<√(s-1)/s for some s≥4/3,then measurement matrix A and linear map A satisfy truncated sparse approximation property of order k.It should be pointed out that when Tc=Ф,our conclusion implies that sparse approximation property of order k is weaker than restricted isometry property of order sk.展开更多
We will prove that for 1<p<∞and 0<λ<n,the central Morrey norm of the truncated centered Hardy-Littlewood maximal operator Mcγequals that of the centered Hardy-Littlewood maximal operator for all 0<γ...We will prove that for 1<p<∞and 0<λ<n,the central Morrey norm of the truncated centered Hardy-Littlewood maximal operator Mcγequals that of the centered Hardy-Littlewood maximal operator for all 0<γ<+∞.When p=1 and 0<λ<n,it turns out that the weak central Morrey norm of the truncated centered Hardy-Littlewood maximal operator Mcγequals that of the centered Hardy-Littlewood maximal operator for all 0<λ<+∞.Moreover,the same results are true for the truncated uncentered Hardy-Littlewood maximal operator.Our work extends the previous results of Lebesgue spaces to Morrey spaces.展开更多
Robust principal component analysis(PCA) is widely used in many applications, such as image processing, data mining and bioinformatics. The existing methods for solving the robust PCA are mostly based on nuclear norm ...Robust principal component analysis(PCA) is widely used in many applications, such as image processing, data mining and bioinformatics. The existing methods for solving the robust PCA are mostly based on nuclear norm minimization. Those methods simultaneously minimize all the singular values, and thus the rank cannot be well approximated in practice. We extend the idea of truncated nuclear norm regularization(TNNR) to the robust PCA and consider truncated nuclear norm minimization(TNNM) instead of nuclear norm minimization(NNM). This method only minimizes the smallest N-r singular values to preserve the low-rank components, where N is the number of singular values and r is the matrix rank. Moreover, we propose an effective way to determine r via the shrinkage operator. Then we develop an effective iterative algorithm based on the alternating direction method to solve this optimization problem. Experimental results demonstrate the efficiency and accuracy of the TNNM method. Moreover, this method is much more robust in terms of the rank of the reconstructed matrix and the sparsity of the error.展开更多
针对过完备字典直接对图像进行稀疏表示不能很好地剔除高频噪声的影响,压缩感知后图像重构质量不高的问题,提出了基于截断核范数低秩分解的自适应字典学习算法。该算法首先利用截断核范数正则化低秩分解模型对图像矩阵低秩分解得到低秩...针对过完备字典直接对图像进行稀疏表示不能很好地剔除高频噪声的影响,压缩感知后图像重构质量不高的问题,提出了基于截断核范数低秩分解的自适应字典学习算法。该算法首先利用截断核范数正则化低秩分解模型对图像矩阵低秩分解得到低秩部分和稀疏部分,其中低秩部分保留了图像的主要信息,稀疏部分主要包含高频噪声及部分物体轮廓信息;然后对图像低秩部分进行分块,依据图像块纹理复杂度对图像块进行分类;最后使用K奇异值分解(K⁃single value decomposition,K⁃SVD)字典学习算法,针对不同类别训练出多个不同大小的过完备字典。仿真结果表明,本文所提算法能够对图像进行较好的稀疏表示,并在很好地保持图像块特征一致性的同时显著提升图像重构质量。展开更多
基金supported by the National Natural Science Foundation of China(11871109)NSAF(U1830107)the Science Challenge Project(TZ2018001)
文摘This paper considers approximately sparse signal and low-rank matrix’s recovery via truncated norm minimization minx∥xT∥q and minX∥XT∥Sq from noisy measurements.We first introduce truncated sparse approximation property,a more general robust null space property,and establish the stable recovery of signals and matrices under the truncated sparse approximation property.We also explore the relationship between the restricted isometry property and truncated sparse approximation property.And we also prove that if a measurement matrix A or linear map A satisfies truncated sparse approximation property of order k,then the first inequality in restricted isometry property of order k and of order 2k can hold for certain different constantsδk andδ2k,respectively.Last,we show that ifδs(k+|T^c|)<√(s-1)/s for some s≥4/3,then measurement matrix A and linear map A satisfy truncated sparse approximation property of order k.It should be pointed out that when Tc=Ф,our conclusion implies that sparse approximation property of order k is weaker than restricted isometry property of order sk.
基金Supported by the National Natural Science Foundation of China(11871452,12071052the Natural Science Foundation of Henan(202300410338)the Nanhu Scholar Program for Young Scholars of XYNU。
基金the National Natural Science Foundation of China(Grant No.11871452)the Project of Henan Provincial Department of Education(No.18A110028)the Nanhu Scholar Program for Young Scholars of XYNU.
文摘We will prove that for 1<p<∞and 0<λ<n,the central Morrey norm of the truncated centered Hardy-Littlewood maximal operator Mcγequals that of the centered Hardy-Littlewood maximal operator for all 0<γ<+∞.When p=1 and 0<λ<n,it turns out that the weak central Morrey norm of the truncated centered Hardy-Littlewood maximal operator Mcγequals that of the centered Hardy-Littlewood maximal operator for all 0<λ<+∞.Moreover,the same results are true for the truncated uncentered Hardy-Littlewood maximal operator.Our work extends the previous results of Lebesgue spaces to Morrey spaces.
文摘数据缺失问题严重影响了智能交通系统中通过数据监控交通态势、预测交通流量、部署交通规划等一系列活动。为此,运用基于张量奇异值分解的低秩张量补全框架提出了加权与截断核范数相结合的交通流数据重构模型WLRTC-TTNN(Low Rank Tensor Completion of Weighted and Truncated Nuclear Norm),该模型可以有效地对缺失的时空交通数据进行修复。WLRTC-TTNN方法主要有两方面的优点:一是加入权重因子解决了原始模型对数据输入方向的依赖问题,实现了模型方向的灵活性;二是运用张量的截断核范数来代替张量的核范数作为张量秩最小化的凸代理,保留了时空交通数据内部主要的特征信息,且根据广义奇异值阈值理论,对较小奇异值进行惩罚处理,进一步优化了模型,最终使用交替乘子法实现了WLRTC-TTNN算法。在两个公开的时空交通数据集上选取不同的缺失场景与缺失率进行实验,结果表明:WLRTC-TTNN的补全性能优于其他基线模型,整体的补全精度提高了3%~37%,在数据极端缺失的情况下,其补全效果更加稳定。
基金the Doctoral Program of Higher Education of China(No.20120032110034)
文摘Robust principal component analysis(PCA) is widely used in many applications, such as image processing, data mining and bioinformatics. The existing methods for solving the robust PCA are mostly based on nuclear norm minimization. Those methods simultaneously minimize all the singular values, and thus the rank cannot be well approximated in practice. We extend the idea of truncated nuclear norm regularization(TNNR) to the robust PCA and consider truncated nuclear norm minimization(TNNM) instead of nuclear norm minimization(NNM). This method only minimizes the smallest N-r singular values to preserve the low-rank components, where N is the number of singular values and r is the matrix rank. Moreover, we propose an effective way to determine r via the shrinkage operator. Then we develop an effective iterative algorithm based on the alternating direction method to solve this optimization problem. Experimental results demonstrate the efficiency and accuracy of the TNNM method. Moreover, this method is much more robust in terms of the rank of the reconstructed matrix and the sparsity of the error.
文摘针对过完备字典直接对图像进行稀疏表示不能很好地剔除高频噪声的影响,压缩感知后图像重构质量不高的问题,提出了基于截断核范数低秩分解的自适应字典学习算法。该算法首先利用截断核范数正则化低秩分解模型对图像矩阵低秩分解得到低秩部分和稀疏部分,其中低秩部分保留了图像的主要信息,稀疏部分主要包含高频噪声及部分物体轮廓信息;然后对图像低秩部分进行分块,依据图像块纹理复杂度对图像块进行分类;最后使用K奇异值分解(K⁃single value decomposition,K⁃SVD)字典学习算法,针对不同类别训练出多个不同大小的过完备字典。仿真结果表明,本文所提算法能够对图像进行较好的稀疏表示,并在很好地保持图像块特征一致性的同时显著提升图像重构质量。
