Uncertainty in Artificial Intelligence
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Learning Riemannian Metrics
Guy Lebanon
Abstract:
We propose a solution to the problem of estimating a Riemannian metric associated with a given differentiable manifold. The metric learning problem is based on minimizing the relative volume of a given set of points. We derive the details for a family of metrics on the multinomial simplex. The resulting metric has applications in text classification and bears some similarity to TFIDF representation of text documents
Keywords:
Pages: 362-369
PS Link:
PDF Link: /papers/03/p362-lebanon.pdf
BibTex:
@INPROCEEDINGS{Lebanon03,
AUTHOR = "Guy Lebanon ",
TITLE = "Learning Riemannian Metrics",
BOOKTITLE = "Proceedings of the Nineteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-03)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2003",
PAGES = "362--369"
}


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