Abstract
This paper introduces a theoretical framework to characterize measures on structured data. We firstly describe the lattice of structured data. Then, four basic and intuitive properties which any measure on structure data must fulfill are formally introduced. Metrics and kernel functions are studied as particular cases of (dis)similarity measures. In the case of metrics we prove that the well-known edit distances meet all the desirable properties. We also give sufficient conditions for a kernel function to satisfy those properties. Some examples are given for particular kinds of structured data.
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Correa-Morris, J., Hernández, N. (2012). On the Comparison of Structured Data. In: Alvarez, L., Mejail, M., Gomez, L., Jacobo, J. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2012. Lecture Notes in Computer Science, vol 7441. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33275-3_59
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DOI: https://doi.org/10.1007/978-3-642-33275-3_59
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