How Many Dissimilarity/Kernel Self Organizing Map Variants Do We Need?

被引：12
作者：
Rossi, Fabrice [1 ]
机构：
[1] Univ Paris 01, SAMM EA 4543, 90 Rue Tolbiac, F-75634 Paris 13, France
来源：
ADVANCES IN SELF-ORGANIZING MAPS AND LEARNING VECTOR QUANTIZATION | 2014年 / 295卷
关键词：
Self Organizing Map; Dissimilarity data; Pairwise data; Kernel; Deterministic annealing; C-MEANS; ALGORITHM; DISTANCES; BATCH; SOM;
D O I：
10.1007/978-3-319-07695-9_1
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In numerous applicative contexts, data are too rich and too complex to be represented by numerical vectors. A general approach to extend machine learning and data mining techniques to such data is to really on a dissimilarity or on a kernel that measures how different or similar two objects are. This approach has been used to define several variants of the Self Organizing Map (SOM). This paper reviews those variants in using a common set of notations in order to outline differences and similarities between them. It discuss the advantages and drawbacks of the variants, as well as the actual relevance of the dissimilarity/kernel SOM for practical applications.
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收藏
页码：3 / 23
页数：21
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