Statistical Translation, Heat Kernels and Expected Distances
Joshua Dillon, Yi Mao, Guy Lebanon, Jian Zhang
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.
PDF Link: /papers/07/p93-dillon.pdf
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"