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Analysis of Neural Data

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Learning Graph Quantization
Citation key Jain2010a
Author Jain, B. and Srinivasan, S. D. and Tissen, A. and Obermayer, K.
Title of Book Structural, Syntactic, and Statistical Pattern Recognition
Pages 109 – 118
Year 2010
ISBN 978-3-642-14979-5, 978-3-642-14980-1
DOI 10.1007/978-3-642-14980-1_10
Note Joint IAPR International Workshop, SSPR&SPR 2010, Cesme, Izmir, Turkey, August 18-20, 2010. Proceedings
Publisher Springer Berlin Heidelberg
Series Lecture Notes in Computer Science
Abstract 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.
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