On Estimating Mutual Information for Feature Selection

被引：0
|
作者：
Schaffernicht, Erik [1 ]
Kaltenhaeuser, Robert [1 ]
Verma, Saurabh Shekhar [2 ]
Gross, Horst-Michael [1 ]
机构：
[1] Ilmenau Univ Technol, Neuroinformat & Cognit Robot Lab, Ilmenau, Germany
[2] Coll Technol, Pantnagar GBPUAT, Uttar Pradesh, India
来源：
ARTIFICIAL NEURAL NETWORKS-ICANN 2010, PT I | 2010年 / 6352卷
关键词：
Mutual Information; Probabilitty Density Estimation; Feature Selection;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Mutual Information (MI) is a powerful concept from information theory used in many application fields. For practical tasks it is often necessary to estimate the Mutual Information from available data. We compare state of the art methods for estimating MI from continuous data, focusing on the usefulness for the feature selection task. Our results suggest that many methods are practically relevant for feature selection tasks regardless of their theoretic limitations or benefits.
引用
收藏
页码：362 / +
页数：2
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