Proximity matrices like kernels or dissimilarity matrices provide nonstandard 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

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... ts and performs favorable to traditional techniques.