High-Dimensional Quadratic Classifiers in Non-sparse Settings

Makoto Aoshima () and Kazuyoshi Yata ()
Additional contact information
Makoto Aoshima: University of Tsukuba
Kazuyoshi Yata: University of Tsukuba

Methodology and Computing in Applied Probability, 2019, vol. 21, issue 3, 663-682

Abstract: Abstract In this paper, we consider high-dimensional quadratic classifiers in non-sparse settings. The quadratic classifiers proposed in this paper draw information about heterogeneity effectively through both the differences of growing mean vectors and covariance matrices. We show that they hold a consistency property in which misclassification rates tend to zero as the dimension goes to infinity under non-sparse settings. We also propose a quadratic classifier after feature selection by using both the differences of mean vectors and covariance matrices. We discuss the performance of the classifiers in numerical simulations and actual data analyzes. Finally, we give concluding remarks about the choice of the classifiers for high-dimensional, non-sparse data.

Keywords: Asymptotic normality; Bayes error rate; Feature selection; Heterogeneity; Large p small n; 62H30; 62H10 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s11009-018-9646-z Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:metcap:v:21:y:2019:i:3:d:10.1007_s11009-018-9646-z

Ordering information: This journal article can be ordered from
https://www.springer.com/journal/11009

DOI: 10.1007/s11009-018-9646-z

Access Statistics for this article

Methodology and Computing in Applied Probability is currently edited by Joseph Glaz

More articles in Methodology and Computing in Applied Probability from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().