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l1benchmark
- 主要用于解决模式识别中稀疏表示人脸识别核心问题L1范数源代码，程序采用同伦算法设计的，在目前稀疏表示多种算法中，同伦算法是性能公认最好的.-Mainly used to solve the sparse representation of face recognition pattern recognition in the core of L1 norm source code, the program designed using the homotopy algorithm, sparse
SL0MatlabCodereal
- 快速的基于光滑的l0范数的过完备的稀疏分解，其速度比L1范数快，恢复效果好-Rapid decomposition overcomplete sparse smooth l0 norm-based, its speed is faster than the L1 norm, restore good effect
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_ls_nonneg
- 最优化问题求解，l1-ls 范数求解matlab程序包.-Optimization problem solving, l1-ls norm solving matlab package.
l1magic
- 实现压缩感知的稀疏信号恢复，采用L1范数约束最小化策略(Sparse signal recovery with compressed sensing, by using the L1 norm constraint minimization strategy)
压缩感知
- 本文分别以稀疏基有离散余弦变换基（DCT）和快速傅立叶变换基（FFT）做为稀疏基，高斯随机矩阵、部分哈达玛矩阵为测量矩阵，L1范数、正交匹配追踪算法（OMP）为重建算法进行压缩感知算法实现。(In this paper, DCT and FFT are used as sparse basis, Gauss random matrix and partial Hadamard matrix are used as measurement matrix, L1 norm and OMP are u