Uncertainty in Artificial Intelligence
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Semi-supervised Learning with Density Based Distances
Avleen Bijral, Nathan Ratliff, Nathan Srebro
Abstract:
We present a simple, yet effective, approach to Semi-Supervised Learning. Our approach is based on estimating density-based distances (DBD) using a shortest path calculation on a graph. These Graph-DBD estimates can then be used in any distance-based supervised learning method, such as Nearest Neighbor methods and SVMs with RBF kernels. In order to apply the method to very large data sets, we also present a novel algorithm which integrates nearest neighbor computations into the shortest path search and can find exact shortest paths even in extremely large dense graphs. Significant runtime improvement over the commonly used Laplacian regularization method is then shown on a large scale dataset.
Keywords:
Pages: 43-50
PS Link:
PDF Link: /papers/11/p43-bijral.pdf
BibTex:
@INPROCEEDINGS{Bijral11,
AUTHOR = "Avleen Bijral and Nathan Ratliff and Nathan Srebro",
TITLE = "Semi-supervised Learning with Density Based Distances",
BOOKTITLE = "Proceedings of the Twenty-Seventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-11)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2011",
PAGES = "43--50"
}


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