STRUCTURED LASSO FOR REGRESSION WITH MATRIX COVARIATES
STRUCTURED LASSO FOR REGRESSION WITH MATRIX COVARIATES
复制标题
用于矩阵协变量回归的结构化套索
DOI：
10.5705/ss.2012.033
复制
发表时间：
2014
影响因子：
1.4
通讯作者：
Chenlei Leng
中科院分区：
文献类型：
--
作者：
Junlong Zhao;Chenlei Leng
高维矩阵数据在现代数据分析中很常见。在对观测值进行矢量化后简单地应用 Lasso 会忽略此类数据中固有的基本行和列信息，从而导致变量选择结果的用处不大。在本文中，我们提出了一种利用结构信息的新方法。该估计易于计算并具有良好的理论特性。与Lasso相比，新的估计可以在较弱的假设下恢复行和列的稀疏结构。仿真表明，与忽略此类信息的方法相比，其在变量选择和收敛速度方面具有更好的性能。医学数据集的应用显示了该提案的有用性。
High-dimensional matrix data are common in modern data analysis. Simply applying Lasso after vectorizing the observations ignores essential row and column information inherent in such data, rendering variable selection results less useful. In this paper, we propose a new approach that takes advantage of the structural information. The estimate is easy to compute and possesses favorable theoretical properties. Compared with Lasso, the new estimate can recover the sparse structure in both rows and columns under weaker assumptions. Simulations demonstrate its better performance in variable selection and convergence rate, compared to methods that ignore such information. An application to a dataset in medical science shows the usefulness of the proposal.