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SOMM – Self-Organized Manifold Mapping

  • Edson Caoru Kitani
  • Emilio Del-Moral-Hernandez
  • Leandro A. Silva
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7553)

Abstract

The Self Organizing Map (SOM) [1] proposed by Kohonen has proved to be remarkable in terms of its range of applications. It can be used for high dimensional space visualization, pattern recognition, input space dimensionality reduction and for generating prototyping to extrapolate information. Basically, tasks conducted by the SOM method are closely related with input space mapping in order to preserve topological and metric relationship between samples. These maps are meant to create a low dimensional output representation of high dimensional input space. Although maps higher than two dimensions can be created by SOM, it is common to work with the limit of one or two dimensions. This work presents a methodology named SOMM (Self-Organized Manifold Mapping) that can be useful to discover structures and clusters of input dataset using the SOM map as a representation of data distribution structure.

Keywords

Manifold Learning Self Organizing Maps Dimensionality Reduction 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Edson Caoru Kitani
    • 1
  • Emilio Del-Moral-Hernandez
    • 1
  • Leandro A. Silva
    • 2
  1. 1.Polytechnic SchoolUniversity of Sao PauloBrazil
  2. 2.School of Computing and InformaticsMackenzie Presbyterian UniversityBrazil

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