Data dimensionality estimation methods: a survey
Data dimensionality estimation methods: a survey
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DOI：
10.1016/s0031-3203(03)00176-6
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发表时间：
2003-12-01
影响因子：
8
通讯作者：
Camastra, F
中科院分区：
文献类型：
--
作者：
Camastra, F
本文对数据维数估计方法进行了综述。数据集的维数估计是模式识别的一个经典问题。文献中有一些不错的综述（《聚类数据算法》，Prentice-Hall, Englewood Cliffs， NJ, 1988），但它们没有包括基于分形技术和神经自关联器的最新发展。本文的目的是提供数据集维数估计方法的最新调查，特别关注基于分形的方法。(C) 2003模式识别学会。Elsevier Ltd.出版。版权所有。
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.