The Path Kernel: A Novel Kernel for Sequential Data

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 318)


We define a novel kernel function for finite sequences of arbitrary length which we call the path kernel. We evaluate this kernel in a classification scenario using synthetic data sequences and show that our kernel can outperform state of the art sequential similarity measures. Furthermore, we find that, in our experiments, a clustering of data based on the path kernel results in much improved interpretability of such clusters compared to alternative approaches such as dynamic time warping or the global alignment kernel.


Kernels Sequences 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Computer Vision and Active Perception Laboratory, Centre for Autonomous SystemsKTH Royal Institute of TechnologyStockholmSweden

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