Model Selection for High-Dimensional Data

被引：0
|
作者：
Owrang, Arash [1 ]
Jansson, Magnus [1 ]
机构：
[1] KTH Royal Inst Technol, ACCESS Linnaeus Ctr, Sch Elect Engn, Stockholm, Sweden
来源：
2016 50TH ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS AND COMPUTERS | 2016年
基金：
瑞典研究理事会;
关键词：
RECOVERY;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
We investigate the task of model selection for high-dimensional data. For this purpose, we propose an extension to the Bayesian information criterion. Our information criterion is asymptotically consistent either as the number of measurements tends to infinity or as the variance of noise decreases to zero. The numerical results provided support our claim. Additionally, we highlight the link between model selection for high-dimensional data and the choice of hyper-parameter in l(1)-constrained estimators, specifically the LASSO.
引用
收藏
页码：606 / 609
页数：4
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