DDα-Classification of Asymmetric and Fat-Tailed Data

被引：12

作者：

Lange, Tatjana ^{[1
]}

Mosler, Karl ^{[2
]}

Mozharovskyi, Pavlo ^{[2
]}

机构：

[1] Hsch Merseburg, Geusaer Str, D-06217 Merseburg, Germany

[2] Univ Cologne, D-50923 Cologne, Germany

来源：

DATA ANALYSIS, MACHINE LEARNING AND KNOWLEDGE DISCOVERY | 2014年

关键词：

DEPTH;

D O I：

10.1007/978-3-319-01595-8_8

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The DD alpha-procedure is a fast nonparametric method for supervised classification of d-dimensional objects into q >= 2 classes. It is based on q-dimensional depth plots and the alpha-procedure, which is an efficient algorithm for discrimination in the depth space [0,1](q). Specifically, we use two depth functions that are well computable in high dimensions, the zonoid depth and the random Tukey depth, and compare their performance for different simulated data sets, in particular asymmetric elliptically and t -distributed data.

引用

页码：71 / 78

页数：8

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