Feature screening for ultrahigh-dimensional binary classification via linear projection

Author:

Lai Peng12,Wang Mingyue1,Song Fengli12,Zhou Yanqiu3

Affiliation:

1. School of Mathematics and Statistics, Nanjing University of Information Science & Technology, Nanjing 210044, China

2. Center for Applied Mathematics of Jiangsu Province, Nanjing University of Information Science & Technology, Nanjing 210044, China

3. School of Science, Guangxi University of Science and Technology, Liuzhou 545006, China

Abstract

<abstract><p>Linear discriminant analysis (LDA) is one of the most widely used methods in discriminant classification and pattern recognition. However, with the rapid development of information science and technology, the dimensionality of collected data is high or ultrahigh, which causes the failure of LDA. To address this issue, a feature screening procedure based on the Fisher's linear projection and the marginal score test is proposed to deal with the ultrahigh-dimensional binary classification problem. The sure screening property is established to ensure that the important features could be retained and the irrelevant predictors could be eliminated. The finite sample properties of the proposed procedure are assessed by Monte Carlo simulation studies and a real-life data example.</p></abstract>

Publisher

American Institute of Mathematical Sciences (AIMS)

Subject

General Mathematics

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