Hard-thresholding Operator on A Covariance MatrixBinhuan Wang
这篇文章主要探讨了如何通过迭代硬阈值算法(Iterative Hard Thresholding, IHT)在稀疏信号恢复中提高计算效率。 文章提出了一种改进的方法,称为正则化迭代硬阈值算法(Normalized Iterative Hard Thresholding),其主要目的是解决传统算法在处理某些条件下的计算不稳定性,特别是矩阵缩放问题。 主要假设条件: 信号稀疏性假设:...
We introduce a novel method for obtaining such an approximate MMSE estimator by replacing the deterministic thresholding operator of Iterative Hard Thresholding with a randomized version. We demonstrate the improvement in performance experimentally for both synthetic 1D signals and real images. 展开 ...
Iterative hard thresholding (IHT) is a class of effective methods to compute sparse solution for underdetermined linear system. In this paper, an efficient IHT method with theoretical guarantee is proposed and named SCIHTBB with attractive features: (1) Monotone and Non-Monotone versions are presente...
Based on fuzzy clustering method, we proposed an unsupervised iterative algorithm which needs only a knowledge of class number, it has the particularity to realize both segmentation and thresholding at the same time. Unlike with nearly all buildings extraction methods, our approach does not require ...
Unfortunately, the hard thresholding operator is independent of the objective function and hence leads to numerical oscillation in the course of iterations. To alleviate this drawback, the hard thresholding operator should be applied to a compressible vector. Motivated by this idea, we propose a new...
Specially when $A$ is the identity matrix such that $\\kappa(A,2k)\\equiv1$, our bound recovers the previously known nearly non-expansive bounds for Euclidean hard thresholding operator. We further show that such a bound extends to an approximate version of $\\mathcal{H}_{A,k}(w)$ ...
We propose two hard thresholding schemes for image reconstruction from compressive samples. The measurements follow an underdetermined linear model, where the regression-coefficient vector is a sum of an unknown deterministic sparse signal component and a zero-mean white Gaussian component with an unknown...
Teager Energy OperatorWavelet Packet TransformStatistical ModelingThrsholding FunctionIn this paper a new thresholding based speech enhancement approach is presented, where thethreshold is statistically determined by employing the Teager energy operation on the Wavelet Packet(WP) coefficients of noisy speech....
We propose one proximal iterative hard thresholding type method with an extrapolation step for acceleration and establish its global convergence results. In detail, the sequence generated by the proposed method globally converges to a local minimizer of the objective function. Finally, we conduct ...