Binning of kernel-based projection pursuit indices in XGobi

被引：2
|
作者：
Klinke, S
Cook, D
机构：
[1] HUMBOLDT UNIV BERLIN, INST STAT & ECONOMETR, D-10178 BERLIN, GERMANY
[2] IOWA STATE UNIV SCI & TECHNOL, DEPT STAT, AMES, IA 50011 USA
来源：
COMPUTATIONAL STATISTICS & DATA ANALYSIS | 1997年 / 25卷 / 03期
关键词：
high-dimensional data analysis; exploratory projection pursuit; fast implementation; kernel density estimation;
D O I：
10.1016/S0167-9473(97)82604-7
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
The software XGobi offers a selection of different index functions to be used in exploratory projection pursuit (EPP). EPP is a method for finding interesting low-dimensional (in this case two-dimensional) projections of high-dimensional data. We discuss the inclusion of two additional index functions which make use of several standard techniques to improve the speed of the indices based on kernel density estimation. Unfortunately, the speed improvements do not apply to derivative calculations, but then are seldomly required in XGobi so speed does not matter much.
引用
收藏
页码：363 / 369
页数：7
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