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使用Overlaps和Graph Lasso组合套索

Group Lasso with Overlaps and Graph Lasso
课程网址: http://videolectures.net/icml09_jacob_glog/  
主讲教师: Laurent Jacob
开课单位: 加州大学伯克利分校
开课时间: 2009-08-26
课程语种: 英语
中文简介:
我们提出了一种新的惩罚函数,当用作经验风险最小化程序的正则化时,会导致稀疏估计。稀疏矢量的支持通常是先验定义的可能重叠的协变量组的联合,或者当给出协变量图时倾向于彼此连接的一组协变量。我们研究估计量的理论性质,并说明其在模拟和乳腺癌基因表达数据上的行为。
课程简介: We propose a new penalty function which, when used as regularization for empirical risk minimization procedures, leads to sparse estimators. The support of the sparse vector is typically a union of potentially overlapping groups of covariates defined a priori, or a set of covariates which tend to be connected to each other when a graph of covariates is given. We study theoretical properties of the estimator, and illustrate its behavior on simulated and breast cancer gene expression data.
关 键 词: 惩罚函数; 经验风险; 稀疏估计
课程来源: 视频讲座网
最后编审: 2019-04-23:lxf
阅读次数: 94