2.2 算法实现 在Python中,可以使用sklearn库中的group_lasso_模块来实现稀疏组套索。下面是一个示例代码: # 引用形式的描述信息:导入必要的库和模块fromsklearn.datasetsimportmake_regressionfromsklearn.linear_modelimportGroupLasso# 引用形式的描述信息:生成一个示例数据集X,y=make_regression(n_samples=100,n_fea...
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asparse-grouplasso一个sparse-group套索 系统标签: lassosparsegroup套索sparsitynonzero ASPARSE-GROUPLASSO NOAHSIMON,JEROMEFRIEDMAN,TREVORHASTIE, ANDROBTIBSHIRANI Abstract.Forhighdimensionalsupervisedlearningproblems, oftenusingproblemspecificassumptionscanleadtogreaterac- curacy.Forproblemswithgroupedcovariates,which...
A note on the group lasso and a sparse group lasso We consider the group lasso penalty for the linear model. We note that the standard algorithm for solving the problem assumes that the model matrices in each group are orthonormal. Here we consider a more general penalty that blends the ....
Direction-of-arrival and power spectral density estimation using a single directional microphone and group-sparse optimization constraint on the residual noise term when solving the group-sparse optimization problem and is referred to as the Group Lasso Least Squares (GL-LS) ... E Tengan,T Dietze...
基于sparsegrouplasso分位数回归的投资组合策略研究AssetsAllocationStrategyBasedonSparseGroupLASSOPenalizedQuantileRegression学位申请人李思琪指导教师陈坤学科专业应用统计学位类别专业学位万方数据
本文将双层变量选择新罚SparseGroupLasso运用到具有相似稀疏结构的整合分析中,并采用一个具有促进稀疏结构相似性作用的罚来促成这样的稀疏结构,然后创建了相应的分块坐标下降求解算法,并提出了若干模型评价指标和参数调优方法.该模型方法成功解决了在预先未知数据集结构但又有一定先验信息可知其具有相似稀疏结构的情况下整合...
Lasso (cFSGL) formulation that allows the simultaneous selection of a common set of biomarkers for multiple time points and specific sets of biomarkers for different time points using the sparse group Lasso penalty and in the meantime incorporates the temporal smoothness using the fused Lasso penalty...
interrelated structures within the MRI features and among the tasks and present a novel Group guided Sparse group lasso (GSGL) regularized multi-task learning approach, to effectively incorporate both the relatedness among multiple cognitive score prediction tasks and useful inherent group structure in ...
Under such settings, variable selection should be conducted at both the group-level and within-group-level, that is, a bi-level selection. In this study, we propose the adaptive sparse group Lasso (adSGL) method, which combines the adaptive Lasso and adaptive group Lasso (GL) to achieve ...