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Abstract: Pairwise data in empirical sciences typically violate metricity, either due to noise or due to fallible estimates, and therefore are hard to analyze by conventional machine learning technology. In this paper we therefore study ways to work around this problem. (Update)
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BibTeX entry: (Update)
V. Roth, J. Laub, J.M. Buhmann, and K.-R. Muller. Going metric: Denoising pairwise data. In NIPS02, 2002. submitted. http://citeseer.ist.psu.edu/roth02going.html More
@misc{ roth02going,
author = "V. Roth and J. Laub and J. Buhmann and K. Muller",
title = "Going metric: Denoising pairwise data",
text = "V. Roth, J. Laub, J.M. Buhmann, and K.-R. Muller. Going metric: Denoising
pairwise data. In NIPS02, 2002. submitted.",
year = "2002",
url = "citeseer.ist.psu.edu/roth02going.html" }
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