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BCS_fast_rvm
- 该代码实现的是压缩感知理论中的信号恢复问题。将压缩感知理论中的信号恢复问题转化为带参数约束的回归问题，从而利用贝叶斯理论实现参数估计，从而得到高效的重建稀疏信号。-The code to achieve the signal recovery problems in the theory of compressed sensing. Recovery issues into regression problems with parameter constraints will signal co
RSC
- 强壮的人脸识别系统，发表于cvpr2011年，程序是应用matlab实现-Recently the sparse representation (or coding) based classifi cation (SRC) has been successfully used in face recognition. In SRC, the testing image is represented as a sparse linear combination of the trai
SVregression
- In kernel ridge regression we have seen the final solution was not sparse in the variables ® . We will now formulate a regression method that is sparse, i.e. it has the concept of support vectors that determine the solution. The thing to not
demo_semisupervised_hyperspectral_segmentation.ra
- sparse multinomial logistic regression
Application-of-interpolation
- 插值的应用：支持向量回归的应用 稀疏的冲击物理数据集的插值-Application of Support Vector Regression to Interpolation of Sparse Shock Physics Data Sets
RSC
- 人脸识别的稀疏表示识别方法将稀疏表示的保真度表示为余项的L2范数，但最大似然估计理论证明这样的假设要求余项服从高斯分布，实际中这样的分布可能并不成立，特别是当测试图像中存在噪声、遮挡和伪装等异常像素，这就导致传统的保真度表达式所构造的稀疏表示模型对上述这些情况缺少足够的鲁棒性。而最大似然稀疏表示识别模型则基于最大似然估计理论，将保真度表达式改写为余项的最大似然分布函数，并将最大似然问题转化为一个加权优化问题-Recently the sparse representation (or codin
s
- Sparse Partial Least Squares Regression for On-Line Variable Selection with Multivariate Data Streams
RVM2
- 基于稀疏贝叶斯框架的机器学习算法，能有效用于回归和分类预测，具有较强的泛化性-Machine learning algorithm based on sparse Bayesian framework, can effectively be used for regression and classification forecast has good generalization
Image-reconstruction_CS
- 合稀疏贝叶斯学习(SBL)和可压缩传感理论(CS)，给出一种在噪声测量条件下重建可压缩图像的方法。该方法将cS理论中图像重建过程看作一个线性回归问题，而待重建的图像是该回归模型巾的未知权值参数；利用sBL方法对权值赋予确定的先验条件概率分布用以限制模型的复杂度，并引入超参数- Hop sparse Bayesian learning ( SBL ) and compressible sensing theory ( CS ) , give a compressible image recon
otimization-problem
- This algorithm solves the otimization problem min_x \| y - A x \|_2^2 + lambda Phi(x) and has applications in compressed sensing, image restoration and reconstruction, sparse regression, and several other problems.
LARS_with_LASSO_modification_0.4
- LARS-lasso 稀疏矩阵 Lassplore for solving Large-scale sparse logistic regression-LARS-lasso sparse representation
LASSOaLARSa-SPCA
- Abstract There a number of interesting variable selection methods available beside the regular forward selection and stepwise selection methods. Such approaches include LASSO (Least Absolute Shrinkage and Selection Operator), least angle regression (
CaiDengcode
- 浙大蔡登，何晓飞写的降维，特征选择等机器学习的源码 包括：谱回归，降维，特征选择，主题模型，矩阵分解，稀疏编码，哈希，聚类，主动学习，矩阵学习。 是一个很好的机器学习源码资料。-cCaideng s code for Machine learning,include Spectral regression : (a regression framework for efficient dimensionality reduction) Dimensionality reduct
RLS-WEIGHTED-LASSO
- 介绍了LASSO算法，他是是一种很好的稀疏信号参数估计方法。-The batch least-absolute shrinkage and selection operator (Lasso) has well-documented merits for estimating sparse signals of inter- est emerging in various applications, where observations adhere to parsimonious
libORF-master
- 针对各种机器学习，深度学习领域的一个matlab工具包-A machine learning library focused on deep learning.Following algorithms and models are provided along with some static utility classes: - Naive Bayes, Linear Regression, Logistic Regression, Softmax Regression, Linear S
colaborative-demo
- 基于协同稀疏表示的解混方法，因为每个端元的相似性，本文采用协同稀疏表示来约束每个像素采用相同位置系数不同的原子-Collaborative Sparse Regression for Hyperspectral Unmixing
gpml-matlab-v3.6-2015-07-07
- 这是一个高斯过程回归和分类工具箱，功能非常齐全，可以为解决高斯过程相关的问题提供很多帮助- GAUSSIAN PROCESS REGRESSION AND CLASSIFICATION Toolbox version 3.6 for GNU Octave 3.2.x and Matlab 7.x Copyright (c) by Carl Edward Rasmussen and Hannes Nickisch, 2015-07-07. 0) HOW
SPP-master
- 稀疏投影保持降维算法，用于高维度数据降维分类和回归的算法-Projections remain sparse dimension reduction algorithm for high-dimensional data dimensionality reduction classification and regression algorithm
LARS算法
- 包括LARS的经典文章和实现代码（MATLAB）(Abstract There are a number of interesting variable selection methods available beside the regular forward selection and stepwise selection methods. Such approaches include lasso (Least Absolute Shrinkage and Selection Operat
Hierarchical sparse priors for regression models
- Sparse regression problems, where it is usually assumed that there are many variables and that the effects of a large subset of variables are negligible, have become increasingly important. This paper describes the construction of hierarchical pri