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. © 2012 Springer-Verlag.
CITATION STYLE
Zhu, X., Schleif, F. M., & Hammer, B. (2012). Patch processing for relational learning vector quantization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7367 LNCS, pp. 55–63). https://doi.org/10.1007/978-3-642-31346-2_7
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