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该源码实现了使用基于稀疏表示的人脸识别算法。使用GPSR作为l1模最小化方法。-This pack of code implement a imges-based face recognition using sparse representation classification. In the algorithm, i employ GPSR as tool to complete the optimization procedure of l1-minimization.
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目前比较流行的稀疏分解重构程序，可以用在人脸识别、字典构造等方面。-Currently popular sparse decomposition and reconstruction process, can be used in face recognition, dictionary structure and so on.
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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
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本程序是利用同伦方法求解L1范数最小化的数值算法-This procedure is the use of homotopy methods to solve the L1-norm minimization numerical algorithm
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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
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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
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TV正则化去卷积the Iteratively Reweighted Norm algorithm for solving the generalized TV functional, which includes the L2-TV and and
L1-TV problems-An Iteratively Reweighted Norm Algorithm for Minimization of Total Variation Functionals
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用reweighted L1优化进行压缩感知的信号重建算法-Optimized by reweighted L1 signal is compressed sensing reconstruction algorithm
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Logistic Loss with the L1-norm Regularization subject to non-negative constraint
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Yall是一个能求解6种不同最小化L1问题的matlab软件包。里面有详细的使用说明和算法求解的基本思路。-It is a Matlab solver that at present can be applied to the six L1-minimization models
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SPGL1 is a Matlab solver for large-scale one-norm regularized least squares.It is designed to solve any of the following three problems: 1. Basis pursuit denoise (BPDN): minimize ||x||_1 subject to ||Ax - b||_2 <= sigma, 2. Basis pursuit (BP): min
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稀疏表达中最小化l1范数问题的求解过程，matlab编写-Sparse expression l1 minimization process of solving the problem of norm, matlab write
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L1范最小化算法，匹配追踪算法，MATLAB语言实现，可以直接用(L1 norm Minimization Algorithm)
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subgrad_method_lin_feas.m---linear feasibility problem
subgrad_method_mtx_complete.m -- positive semidefinite matrix completion
subgrad_method_least_l1.m -- least l1-norm minimization
subgrad_method_dual_prob.m -- solving dual problem using
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Maltlab program for Compressive sensing using L1 minimization
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