Skip to Main content Skip to Navigation
Conference papers

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

Abstract : 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 discusses the advantages and drawbacks of the variants, as well as the actual relevance of the dissimilarity/kernel SOM for practical applications.
Complete list of metadatas

Cited literature [6 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01017468
Contributor : Fabrice Rossi <>
Submitted on : Wednesday, July 2, 2014 - 3:36:24 PM
Last modification on : Sunday, January 19, 2020 - 6:38:32 PM
Long-term archiving on: : Thursday, October 2, 2014 - 11:40:52 AM

Identifiers

Collections

Citation

Fabrice Rossi. How Many Dissimilarity/Kernel Self Organizing Map Variants Do We Need?. 10th International Workshop on Self Organizing Maps, WSSOM 2014, Jul 2014, Mittweida, Germany. pp.3-23, ⟨10.1007/978-3-319-07695-9_1⟩. ⟨hal-01017468⟩

Share

Metrics

Record views

316

Files downloads

607