使用Overlaps和Graph Lasso组合套索][Group Lasso with Overlaps and Graph Lasso]

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:	118

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