Uncertainty in Artificial Intelligence
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Learning from Point Sets with Observational Bias
Liang Xiong, Jeff Schneider
Abstract:
Many objects can be represented as sets of multi- dimensional points. A common approach to learning from these point sets is to assume that each set is an i.i.d. sample from an unknown un- derlying distribution, and then estimate the sim- ilarities between these distributions. In realistic situations, however, the point sets are often sub- ject to sampling biases due to variable or incon- sistent observation actions. These biases can fun- damentally change the observed distributions of points and distort the results of learning. In this paper we propose the use of conditional diver- gences to correct these distortions and learn from biased point sets effectively. Our empirical study shows that the proposed method can successfully correct the biases and achieve satisfactory learn- ing performance.
Keywords:
Pages: 898-906
PS Link:
PDF Link: /papers/14/p898-xiong.pdf
BibTex:
@INPROCEEDINGS{Xiong14,
AUTHOR = "Liang Xiong and Jeff Schneider",
TITLE = "Learning from Point Sets with Observational Bias",
BOOKTITLE = "Proceedings of the Thirtieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-14)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2014",
PAGES = "898--906"
}


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