Data dimensionality estimation methods: a survey

被引：176

作者：

Camastra, F ^{[1
]}

机构：

[1] Univ Genoa, DISI, INFM, I-16146 Genoa, Italy

来源：

PATTERN RECOGNITION | 2003年 / 36卷 / 12期

关键词：

intrinsic dimensionality; topological dimension; Fukunaga-Olsen's algorithm; fractal dimension; multidimensional scaling;

D O I：

10.1016/S0031-3203(03)00176-6

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, data dimensionality estimation methods are reviewed. The estimation of the dimensionality of a data set is a classical problem of pattern recognition. There are some good reviews (Algorithms for Clustering Data, Prentice-Hall, Englewood Cliffs, NJ, 1988) in literature but they do not include more recent developments based on fractal techniques and neural autoassociators. The aim of this paper is to provide an up-to-date survey of the dimensionality estimation methods of a data set, paying special attention to the fractal-based methods. (C) 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

引用

页码：2945 / 2954

页数：10

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