使用贪婪算法mp对图像进行重构 实验效果非常的好

An Algorithm for Designing of Overcomplete Dictionaries for Sparse Representation论文代码

rpca matlab代码稀疏数据的快速随机PCA算法程序 最新更新提示： 1.我们将编译文件修改为更简单的版本。 用户可以使用“ source compile.sh”并选择“ icc”或“ gcc”编译器来编译程序。 2.我们在代码中添加了传递参数“ q”的注释，传递参数q应该大于1，并且q次传递等于（q-2）/ 2次幂迭代。 1.主要算法 1.matlab / eigSVD.m ----通过Matlab的[1]中的特征分解来进行经济/截断奇异值分解的算法，参数k用于截断奇异值分解 2.matlab / frPCA.m ----由Matlab在[1]中实现的用于稀疏数据的快速随机PCA算法，参数模式用于初始数据矩阵的不同大小 3.icc / frpca.c ---- [1]中的frPCA和frPCAt算法是通过ICC和OpenMP实现的，[2]中的基本rPCA包含在文件中。 2.测试实验 （1）ICC编译器或MKL库需要Intel MKL [3]的支持，并且在准备好所有内容后，运行“ source compile.sh”并选择编译器（“ icc”或“ gcc”），然后可执行程序将被编

A Weighted Sparse Coding Framework for Saliency Detection, Nianyi Li, Bilin Sun, Jingyi Yu, CVPR 2015----code

This textbook introduces sparse and redundant representations with a focus on applications in signal and image processing. The theoretical and numerical foundations are tackled before the applications are discussed. Mathematical modeling for signal sources is discussed along with how to use the proper model for tasks such as denoising, restoration, separation, interpolation and extrapolation, compression, sampling, analysis and synthesis, detection, recognition, and more. The presentation is elegant and engaging. Sparse and Redundant Representations is intended for graduate students in applied mathematics and electrical engineering, as well as applied mathematicians, engineers, and researchers who are active in the fields of signal and image processing.

稀疏阵列，阵列方向图，将阵列天线的波束形成问题等效为求解稀疏信号向 量的最优化问题的方法，快速准确地得到最大稀疏化的阵列，以及阵元位置和激励幅度

A Fast Proximal Method for Convolutional Sparse Coding.pdf

Simple and Practical Algorithm for Sparse Fourier Transform SFT经典文章一篇

斯坦福大学机器学习课程，exercise代码。经验证，没错误。

稀疏矩阵 稀疏矩阵库。 安装 $ npm i ml-sparse-matrix 用法 import { SparseMatrix } from "ml-sparse-matrix" ; const matrix1 = new SparseMatrix ( [ [ 1 , 2 ] , [ 3 , 4 ] , ] ) ; const matrix2 = new SparseMatrix ( [ [ 0 , 5 ] , [ 6 , 7 ] , ] ) ; const product = matrix1 . kroneckerProduct ( matrix2 ) ; 执照