Learning to Classify With Possible Sensor Failures
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 noncorrupted measurements while controlling the false alarm rate associated with anomalous samples. By incorporating a nonparametric 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.