Online and Batch Learning Algorithms for Data with Missing Features

Details Online and Batch Learning Algorithms for Data with Missing Features Online and Batch Learning Algorithms for Data with Missing Features

2011-04-05

arxiv.org/abs/1104.0729v4

27th Conference on Uncertainty in Artificial Intelligence (UAI 2011)

Computer Science

Learning

Scientific paper

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 relation 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 baselines.

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