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.
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Zhu, X., Schleif, FM., Hammer, B. (2012). Patch Processing for Relational Learning Vector Quantization. In: Wang, J., Yen, G.G., Polycarpou, M.M. (eds) Advances in Neural Networks – ISNN 2012. ISNN 2012. Lecture Notes in Computer Science, vol 7367. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31346-2_7
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DOI: https://doi.org/10.1007/978-3-642-31346-2_7
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