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Fitting a graph to vector data

Author(s)
Daitch, Samuel I.; Kelner, Jonathan Adam
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Attribution-Noncommercial-Share Alike 3.0 Unported http://creativecommons.org/licenses/by-nc-sa/3.0/
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Abstract
We introduce a measure of how well a combinatorial graph ts a collection of vectors. The optimal graphs under this measure may be computed by solving convex quadratic programs and have many interesting properties. For vectors in d dimensional space, the graphs always have average degree at most 2(d+1), and for vectors in 2 dimensions they are always planar. We compute these graphs for many standard data sets and show that they can be used to obtain good solutions to classifi cation, regression and clustering problems.
Date issued
2009-01
URI
http://hdl.handle.net/1721.1/60697
Department
Massachusetts Institute of Technology. Department of Mathematics
Journal
Proceedings of the 26th Annual International Conference on Machine Learning
Publisher
Association for Computing Machinery
Citation
Samuel I. Daitch, Jonathan A. Kelner, and Daniel A. Spielman. 2009. Fitting a graph to vector data. In Proceedings of the 26th Annual International Conference on Machine Learning (ICML '09). ACM, New York, NY, USA, 201-208. DOI=10.1145/1553374.1553400 http://doi.acm.org/10.1145/1553374.1553400
Version: Author's final manuscript
ISBN
978-1-60558-516-1

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