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

Fabrice Rossi (1)

1 SAMM - Statistique, Analyse et Modélisation Multidisciplinaire (SAmos-Marin Mersenne)

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.

Mots clés: Deterministic annealing; Self Organizing Map; Dissimilarity data; Pairwise data; Kernel

Domaines: Machine Learning [stat.ML]; Apprentissage [cs.LG]; Réseau de neurones [cs.NE]

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⟩