OPTIMAL BAYESIAN FEATURE SELECTION WITH MISSING DATA

被引：0
|
作者：
Pour, Ali Foroughi [1 ]
Dalton, Lori A. [1 ]
机构：
[1] Ohio State Univ, Columbus, OH 43210 USA
来源：
2016 IEEE GLOBAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING (GLOBALSIP) | 2016年
关键词：
Feature selection; Biomarker discovery; Missing data;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
We present a framework for optimal Bayesian feature selection and missing value estimation. Based on this framework, we derive optimal algorithms under an independent Gaussian model, and provide fast sub-optimal methods with superb performance for a dependent Gaussian model.
引用
收藏
页码：35 / 39
页数：5
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