.png){kind=icon}
Return
Data dimensionality estimation methods: a survey
.png){kind=icon}
OA
AI
DOI：10.1016/S0031-3203(03)00176-6.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
intrinsic dimensionality
topological dimension
Fukunaga-Olsen's algorithm
fractal dimension
multidimensional scaling
Journal
IF:
7.6
Papers:
1.4W
Citations:
4.5W
Organization
No organization information available
Cited Papers
.png){kind=icon}
ShareSave
ShareSaveShareSaveShareSaveShareSaveShareSave
View more