Sparse Online Self-Organizing Maps for Large Relational Data

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 428)

Abstract

During the last decades, self-organizing maps were proven to be useful tools for exploring data. While the original algorithm was designed for numerical vectors, the data became more and more complex, being frequently too rich to be described by a fixed set of numerical attributes. Several extensions of the original SOM were proposed in the literature for handling kernel or dissimilarity data. Most of them use the entire kernel/dissimilarity matrix, which requires at least quadratic complexity and becomes rapidly unfeasible for 100 000 inputs, for instance. In the present manuscript, we propose a sparse version of the online relational SOM, which sequentially increases the composition of the prototypes.

Keywords

Relational data Online relational SOM Sparse approximations 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.SAMM - Université Paris 1 Panthéon-SorbonneParisFrance
  2. 2.INRA, UR 0875 MIATCastanet TolosanFrance

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