2014 | OriginalPaper | Chapter
Discriminative Fast Soft Competitive Learning
Author : Frank-Michael Schleif
Published in: Artificial Neural Networks and Machine Learning – ICANN 2014
Publisher: Springer International Publishing
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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.