Binary discrimination methods for high-dimensional data with a geometric representation

DOI10.1080/03610926.2017.1342838OpenAlexW2730089237MaRDI QIDQ5160208

Addy Bolivar-Cime, L. M. Cordova-Rodriguez

Publication date: 28 October 2021

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610926.2017.1342838

zbMATH Keywords

asymptotic analysishigh-dimensional datageometric representationmachine learningbinary discrimination

Mathematics Subject Classification ID

Asymptotic distribution theory in statistics (62E20) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (2)

A classifier under the strongly spiked eigenvalue model in high-dimension, low-sample-size context ⋮ More about asymptotic properties of some binary classification methods for high dimensional data

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Distance-Weighted Discrimination
• Boundary behavior in high dimension, low sample size asymptotics of PCA
• A distance-based, misclassification rate adjusted classifier for multiclass, high-dimensional data
• Effective PCA for high-dimension, low-sample-size data with noise reduction via geometric representations
• PCA consistency in high dimension, low sample size context
• Comparison of binary discrimination methods for high dimension low sample size data
• Support vector machine and its bias correction in high-dimension, low-sample-size settings
• Scale adjustments for classifiers in high-dimensional, low sample size settings
• Weighted Distance Weighted Discrimination and Its Asymptotic Properties
• The maximal data piling direction for discrimination
• Geometric Representation of High Dimension, Low Sample Size Data
• The high-dimension, low-sample-size geometric representation holds under mild conditions

This page was built for publication: Binary discrimination methods for high-dimensional data with a geometric representation