Discriminative Fast Soft Competitive Learning

  • Frank-Michael Schleif
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8681)


Proximity matrices like kernels or dissimilarity matrices provide non-standard data representations common in the life science domain. Here we extend fast soft competitive learning to a discriminative and vector labeled learning algorithm for proximity data. It provides a more stable and consistent integration of label information in the cost function solely based on a give proximity matrix without the need of an explicite vector space. The algorithm has linear computational and memory requirements and performs favorable to traditional techniques.


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© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Frank-Michael Schleif
    • 1
  1. 1.School of Computer ScienceUniversity of BirminghamBirminghamUK

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