搜索资源列表
L1-Norm 优化求解
- 稀疏性方程组的优化求解算法，利用L1-Norm求解最稀疏的方程组解。主要用于压缩感知领域。
l1_OMP_matlab 压缩感知 L1范数最小化算法正交匹配追踪法重构信号
- 压缩感知 L1范数最小化算法正交匹配追踪法重构信号-compressive sensing L1-norm
l1magic
- 压缩感知中求解最优L1范数问题的BP算法内含指导文章-Compressed sensing in L1 norm to solve the problem of optimal BP algorithm article contains guidance
l1magic-1.1
- 最小化L1范数求解，通过L1-LS工具包。-L1 norm minimization solution, through the L1-LS kit.
Source
- l1-norm, compress sensing
LogisticR
- Logistic Loss with the L1-norm Regularization
l1-slove
- 压缩感知中求解最优L1范数问题的BP算法内含指导文章-Compressed sensing in L1 norm to solve the problem of optimal BP algorithm article contains guidance
NESTA_v1.0
- NESTA，非常好的优化算法,The algorithm uses two ideas due to Yurii Nesterov. The first idea is an accelerated convergence scheme for first-order methods, giving the optimal convergence rate for this class of problems. The second idea is a smoothing technique t
l1benchmark
- l1benchmark 这个算法包提供了十种求解带稀疏约束的矩阵方程 AX=b 的 MATLAB 实现代码，并提供了一个比较各种算法求解结果的演示。-An L1-norm minimization benchmark package, which contains an implementation of ten L1-norm minimization algorithms in MATLAB. The package also provides a test scr ipt for comp
l1magic
- This package contains code for solving seven optimization problems. -The main directory contains MATLAB m-files which contain simple examples for each of the recovery problems. They illustrate how the code should be used (it is fairly straightfor
l1_ls_matlab
- 基于BP算法的 求解最优L1范数的程序和文章-BP algorithm based on L1 norm for solving optimal procedures and articles
LeastR
- LeastR Least Squares Loss with the L1-norm Regularization-Least Squares Loss with the L1-norm Regularization
nnLeastR
- Least Squares Loss with the L1-norm Regularization subject to non-negative constraint
nnLogisticR
- Logistic Loss with the L1-norm Regularization subject to non-negative constraint
Approximation-method--BPs-l1-norm
- 基于BP的l1范数逼近法-Approximation method based on BP s l1 norm
l1-norm-recovery
- the recovery of the 2D SAR image with l1-norm minimization
l1-Norm-Minimization
- 该文章介绍了L1范数最小化问题稀疏求解的快速算法-This article describes a quick way L1 norm minimization problem solving sparse
L1 SVD
- 利用压缩感知实现波达方向估计，运用奇异值分解对接收信号进行降维，再利用L1范数进行估计(The DOA estimation is realized by compressed sensing, and the singular value decomposition is used to reduce the received signal, and then the L1 norm is used to estimate the DOA)
l1-uwa-master
- 感觉这份代码中最有价值的应该是水声信道的建模，该源码来自美国 Parastoo Qarabaqi, Northeastern University, 2013其次其提供了基于l1范数下PCA处理算法，来对经由水声信道的信号进行处理给出误码率图，启动比较程序需要先使用信道生成函数对信号进行生成。(The most valuable part of this code is the modeling of underwater acoustic channel. Secondly, it provi
L1范数代码
- 动态压缩感知（DSC）是压缩感知领域中一个重要的研究分支，它是近几年新兴起的一种信号处理与分析方法，与传统的压缩感知理论不同，DSC研究的对象是稀疏时变信号，并且已在视频信号处理和动态核磁共振成像等方面显示出了强大的应用潜力。本节正是在此基础上，提出了一种用于多普勒频率跟踪估计的DSC方法。首先，通过前一跟踪时刻所得到的先验DOA稀疏信息，获得当前跟踪时刻信号向量中各位置非零元素的分布概率，继而建立起动态DOA的稀疏概率模型。然后，采用加权l_1范数最小化方法重构出当前跟踪时刻的信号向量，从而确