Abstract
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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Schleif, FM. (2014). Discriminative Fast Soft Competitive Learning. In: Wermter, S., et al. Artificial Neural Networks and Machine Learning – ICANN 2014. ICANN 2014. Lecture Notes in Computer Science, vol 8681. Springer, Cham. https://doi.org/10.1007/978-3-319-11179-7_11
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DOI: https://doi.org/10.1007/978-3-319-11179-7_11
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