Feature subset selection and ranking for data dimensionality reduction

被引：140

作者：

Wei, Hua-Liang ^{[1
]}

Billings, Stephen A. ^{[1
]}

机构：

[1] Univ Sheffield, Dept Automat Control & Syst Engn, Sheffield S1 3JD, S Yorkshire, England

来源：

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE | 2007年 / 29卷 / 01期

关键词：

dimensionality reduction; feature selection; high-dimensional data;

D O I：

10.1109/TPAMI.2007.250607

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A new unsupervised forward orthogonal search (FOS) algorithm is introduced for feature selection and ranking. In the new algorithm, features are selected in a stepwise way, one at a time, by estimating the capability of each specified candidate feature subset to represent the overall features in the measurement space. A squared correlation function is employed as the criterion to measure the dependency between features and this makes the new algorithm easy to implement. The forward orthogonalization strategy, which combines good effectiveness with high efficiency, enables the new algorithm to produce efficient feature subsets with a clear physical interpretation.

引用

页码：162 / 166

页数：5

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