  In this paper, we propose a general framework to learn a robust large-margin
binary classifier when corrupt measurements, called anomalies, caused by sensor
failure might be present in the training set. The goal is to minimize the
generalization error of the classifier on non-corrupted measurements while
controlling the false alarm rate associated with anomalous samples. By
incorporating a non-parametric regularizer based on an empirical entropy
estimator, we propose a Geometric-Entropy-Minimization regularized Maximum
Entropy Discrimination (GEM-MED) method to learn to classify and detect
anomalies in a joint manner. We demonstrate using simulated data and a real
multimodal data set. Our GEM-MED method can yield improved performance over
previous robust classification methods in terms of both classification accuracy
and anomaly detection rate.
