This contribution extends learning vector quantization to the domain of graphs. For this, we first identify graphs with points in some orbifold, then derive a generalized differentiable intrinsic metric, and finally extend the update rule of LVQ for generalized differentiable distance metrics. First experiments indicate that the proposed approach can perform comparable to state-of-the-art methods in structural pattern recognition. © 2010 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Jain, B. J., Srinivasan, S. D., Tissen, A., & Obermayer, K. (2010). Learning graph quantization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6218 LNCS, pp. 109–118). https://doi.org/10.1007/978-3-642-14980-1_10
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