The Path Kernel: A Novel Kernel for Sequential Data

Andrea Baisero, Florian T. Pokorny, Danica Kragic, Carl Henrik Ek
In Pattern Recognition Applications and Methods, 2015, pp. 71-84


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.


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@incollection{baisero2015a, year={2015}, isbn={978-3-319-12609-8}, booktitle={Pattern Recognition Applications and Methods}, volume={318}, series={Advances in Intelligent Systems and Computing}, editor={Fred, Ana and De Marsico, Maria}, doi={10.1007/978-3-319-12610-4_5}, title={The Path Kernel: A Novel Kernel for Sequential Data}, url={}, publisher={Springer International Publishing}, keywords={Kernels; Sequences}, author={Baisero, Andrea and Pokorny, Florian T. and Kragic, Danica and Ek, Carl Henrik}, pages={71-84}, language={English} }