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Patch Processing for Relational Learning Vector Quantization

  • Xibin Zhu
  • Frank-Michael Schleif
  • Barbara Hammer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7367)

Abstract

Recently, an extension of popular learning vector quantization (LVQ) to general dissimilarity data has been proposed, relational generalized LVQ (RGLVQ) [10,9]. An intuitive prototype based classification scheme results which can divide data characterized by pairwise dissimilarities into priorly given categories. However, the technique relies on the full dissimilarity matrix and, thus, has squared time complexity and linear space complexity. In this contribution, we propose an intuitive linear time and constant space approximation of RGLVQ by means of patch processing. An efficient heuristic which maintains the good classification accuracy and interpretability of RGLVQ results, as demonstrated in three examples from the biomdical domain.

Keywords

Gaussian Mixture Model Vector Quantization Dissimilarity Matrix Learn Vector Quantization Stochastic Gradient Descent 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Xibin Zhu
    • 1
  • Frank-Michael Schleif
    • 1
  • Barbara Hammer
    • 1
  1. 1.CITEC centre of excellenceBielefeld UniversityBielefeldGermany

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