.png){kind=icon}
返回
Group-sparse regression using the covariance fitting criterion
.png){kind=icon}
PRE
AI
DOI：10.1016/j.sigpro.2017.03.025.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this work, we present a novel formulation for efficient estimation of group-sparse regression problems. By relaxing a covariance fitting criteria commonly used in array signal processing, we derive a generalization of the recent SPICE method for grouped variables. Such a formulation circumvents cumbersome model order estimation, while being inherently hyperparameter-free. We derive an implementation which iteratively decomposes into a series of convex optimization problems, each being solvable in closed-form. Furthermore, we show the connection between the proposed estimator and the class of LASSO-type estimators, where a dictionary-dependent regularization level is inherently set by the covariance fitting criteria. We also show how the proposed estimator may be used to form group-sparse estimates for sparse groups, as well as validating its robustness against coherency in the dictionary, i.e., the case of overlapping dictionary groups. Numerical results show preferable estimation performance, on par with a group-LASSO bestowed with oracle regularization, and well exceeding comparable greedy estimation methods. (C) 2017 Published by Elsevier B.V.
Keyword:
Covariance fitting
SPICE
Group sparsity
Group-LASSO
Hyperparameter-free
Convex optimization
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
10.0K
被引数:
1.7W
机构
引用论文
Genetic association study of the GNB3 C825T, the ACE I/D and the eNOS G894T polymorphisms and the risk to develop erectile dysfunction in a German ED population.png)
Andrologia
OAAI
AndrologiaIF0
OAAI分享收藏
分享收藏MONOTONIC CONVERGENCE OF A GENERAL ALGORITHM FOR COMPUTING OPTIMAL DESIGNSANNALS OF STATISTICSOAAI
ANNALS OF STATISTICSIF3.7
OAAI分享收藏分享收藏
查看更多内容