Uncertainty in Artificial Intelligence
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Learning with Missing Features
Afshin Rostamizadeh, Alekh Agarwal, Peter Bartlett
Abstract:
We introduce new online and batch algorithms that are robust to data with missing features, a situation that arises in many practical applications. In the online setup, we allow for the comparison hypothesis to change as a function of the subset of features that is observed on any given round, extending the standard setting where the comparison hypothesis is fixed throughout. In the batch setup, we present a convex relaxation of a non-convex problem to jointly estimate an imputation function, used to fill in the values of missing features, along with the classification hypothesis. We prove regret bounds in the online setting and Rademacher complexity bounds for the batch i.i.d. setting. The algorithms are tested on several UCI datasets, showing superior performance over baseline imputation methods.
Keywords:
Pages: 635-642
PS Link:
PDF Link: /papers/11/p635-rostamizadeh.pdf
BibTex:
@INPROCEEDINGS{Rostamizadeh11,
AUTHOR = "Afshin Rostamizadeh and Alekh Agarwal and Peter Bartlett",
TITLE = "Learning with Missing Features",
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 = "635--642"
}


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