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- Sparse Partial Least Squares Regression for On-Line Variable Selection with Multivariate Data Streams
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
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 (
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
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
A novel illumination-robust local descriptor
- Introduce a novel illumination-robust local descr iptor named Sparse Linear Regression Binary (SLRB) descr iptor.
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