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Statistical Translation, Heat Kernels and Expected Distances
Joshua Dillon, Yi Mao, Guy Lebanon, Jian Zhang
Abstract:
High dimensional structured data such as text and images is often poorly understood and misrepresented in statistical modeling. The standard histogram representation suf- fers from high variance and performs poorly in general. We explore novel connections between statistical translation, heat kernels on manifolds and graphs, and expected dis- tances. These connections provide a new framework for unsupervised metric learning for text documents. Experiments indicate that the resulting distances are generally su- perior to their more standard counterparts.
Keywords:
Pages: 93-100
PS Link:
PDF Link: /papers/07/p93-dillon.pdf
BibTex:
@INPROCEEDINGS{Dillon07,
AUTHOR = "Joshua Dillon
and Yi Mao and Guy Lebanon and Jian Zhang",
TITLE = "Statistical Translation, Heat Kernels and Expected Distances",
BOOKTITLE = "Proceedings of the Twenty-Third Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-07)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2007",
PAGES = "93--100"
}
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