Florian T. Pokorny
Assistant Professor, School of Computer Science and Communication, KTH Royal Institute of TechnologyThe Path Kernel: A Novel Kernel for Sequential Data
In Pattern Recognition Applications and Methods, 2015, pp. 71-84
Abstract
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={http://link.springer.com/chapter/10.1007/978-3-319-12610-4_5},
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}
}