Using mathematical morphology for unsupervised classification of functional data

被引：6

作者：

Leon, T. ^{[1
]}

Ayala, G. ^{[1
]}

Gaston, M. ^{[2
]}

Mallor, F. ^{[2
]}

机构：

[1] Univ Valencia, Dept Stat & Operat Res, Burjassot 46100, Spain

[2] Univ Publ Navarra, Dept Stat & Operat Res, Pamplona 31006, Spain

来源：

JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION | 2011年 / 81卷 / 08期

关键词：

functional data; mathematical morphology; unsupervised classification; PATTERN SPECTRA; SHAPE; CURVES;

D O I：

10.1080/00949651003596099

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

This paper is concerned with the unsupervised classification of functional data by using mathematical morphology. Different morphological operators are used to extract relevant structures of the functions (considered as sets through their subgraph representations). These operators can be considered as preprocessing tools whose outputs are also functional data. We explore some dissimilarity measures and clustering methods for the classification of the transformed data. Our approach is illustrated through a detailed analysis of two data sets. These techniques, which have mainly been used in image processing, provide a flexible and robust toolbox for improving the results in unsupervised functional data classification.

引用

页码：1001 / 1016

页数：16

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