Intrinsic dimensionality estimation with optimally topology preserving maps

被引：80

作者：

Bruske, J ^{[1
]}

Sommer, G ^{[1
]}

机构：

[1] Univ Kiel, Inst Comp Sci, D-24105 Kiel, Germany

来源：

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE | 1998年 / 20卷 / 05期

关键词：

intrinsic dimensionality estimation; topology preservation; principal component analysis; vector quantization;

D O I：

10.1109/34.682189

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A new method for analyzing the intrinsic dimensionality (ID) of low-dimensional manifolds in high-dimensional feature spaces is presented. Compared to a previous approach by Fukunaga and Olsen, the method has only linear instead of cubic time complexity w.r.t. the dimensionality of the input space. Moreover, it is less sensitive to noise than the former approach. Experiments include ID estimation of synthetic data for comparison and illustration as well as ID estimation of an image sequence.

引用

页码：572 / 575

页数：4

共 19 条

[1]

[Anonymous], COLLECTED WORKS LEJ

[2]

[Anonymous], SELFORGANIZING MAPS

[3] INTRINSIC DIMENSIONALITY OF SIGNAL COLLECTIONS [J].